Modes of climate variability
Modes of climate variability are critical patterns in the Earth's climate system that significantly influence weather and climate conditions across different regions. These modes, often described as "seesaws," represent the interplay of climatic elements like temperature, atmospheric pressure, and precipitation. For instance, during El Niño events, certain areas may experience warmer and wetter conditions, while others face cooler and drier weather, illustrating the contrasting effects characteristic of these seesaws.
The El Niño-Southern Oscillation (ENSO) is one of the most recognized modes, with a cycle averaging four years, though its duration can vary. Other important modes include the Pacific Decadal Oscillation (PDO), which operates on a much longer timescale of twenty to fifty years. These modes demonstrate how interconnected the global climate system is, where changes in one region can have far-reaching impacts elsewhere.
Understanding these modes is essential for improving climate predictions and interpreting climatic data accurately. Although significant advancements have been made in identifying and analyzing these patterns, the science remains complex, with many uncertainties surrounding their causes and behaviors. Climate scientists continue to explore the dynamics of these modes to enhance our understanding of climate variability and its implications for future climate change.
Modes of climate variability
Modern technologies, particularly satellites and supercomputers, have allowed scientists to view climate from a global perspective, revealing recurrent patterns of climate parameters that affect large areas of the planet over varying periods of time, often years or decades.
Background
A college town with 2,000 residents that hosts 10,000 students for eight months every year has an average population of 8,666, but it will almost never have that number present. In “academic year” mode, it has a population of 12,000, and in “vacation” mode, it has 2,000. The climate also has modes, and its behavior varies dramatically between them. Temperatures, rainfall, winds, and other climatic phenomena during El Niño are very different from those during La Niña. Recognizing these modes is essential to understanding how the climate operates.
Seesaws
Modes of climate variability are often referred to as “seesaws” because what is missing from one region (such as warmth, atmospheric pressure, or precipitation) is found in excess in the other region. Such seesaws result from the fact that the atmosphere is a finite body of gas that obeys the laws of physics.
By 1924, a number of seesaws had been identified by Sir Gilbert Walker. The data set he presented in 1932 had 183 stations widely spaced across the globe, with multiyear records that permitted statistical analysis. He put his North Atlantic Oscillation (NAO) and North Pacific Oscillation (NPO) on a statistical footing, detailing the strength of correlations between what he called “action centers.” He also established the existence of the Southern Oscillation, which he defined in terms of a pressure seesaw. By the 1960s, others had shown that this coincided with a pattern of sea surface temperature fluctuations called El Niño, and so it is now known as the El Niño-Southern Oscillation (ENSO).
Additional workers found more seesaws, and often an index was determined by combining the values of some climatological variable at two or more locations in a simple algebraic way: The Southern Oscillation Index was obtained by subtracting the sea-level barometric pressure at Darwin, Australia, from that at Tahiti; the North Atlantic Oscillation Index was obtained by subtracting the sea-level pressure at Iceland from that at the Azores.
Statistical Identification of Seesaws
Since Walker’s work, the data series from many of his stations have been extended by more than seventy years, hundreds of new stations have been established, and satellite and other remote-sensing techniques have contributed immense amounts of climate information. New statistical techniques involving eigenvector analysis have been developed to analyze these data, particularly Principal Component (PC)/Empirical Orthogonal Function (EOF) analysis.
These techniques take data sets, which can be enormous, and rearrange them into separate, independent components that reveal how the data points are linked. As an example, consider the pressure data for points in the Northern Hemisphere, but outside the tropics (that is, at latitudes greater than 20° north). Eigenvector analysis finds that much of the variability in these data can be explained by two regional seesaws, EOF1, and EOF2. A map of which regions are controlled by each EOF shows that EOF1 corresponds to the NAM, and EOF2 corresponds to the PNA. Because this representation of PNA uses criteria that differ from its original index-based definition, it is often referred to as PNA.
Context
Teleconnections show that Earth’s climate is not entirely random. Spatial patterns exist, and the polarity of the seesaws within these patterns alternates, often with far-reaching consequences. What is less well known is the temporal behavior of these patterns, what causes them to switch polarity, and how they interact.
ENSO is the shortest and best-known seesaw, having an average period of four years, but this period can vary from two to seven years. Efforts to explain why its period should change, or to predict how long a particular El Niño or La Niña will last, have so far been unsuccessful.
The PDO has effects that are geographically similar to those of the ENSO, but has a period of twenty to fifty years. Cool before 1924, then warm until 1947, cool again until 1976, and warm again until at least 1998, it has had ambiguous behavior since then. In addition to not knowing why it reverses or when it might reverse again, climatologists do not know whether the PDO causes increased ENSO fluctuations or is caused by them.
Scientists’ understanding of the modes of climate variability is incomplete, but most climate scientists agree that they play an important role over periods of years to decades. Our ability to interpret climate data correctly depends on being able to place them in context with respect to these modes. Predictions of climate change would improve if it were possible to predict when these modes will reverse and how strong they would be.
Key Concepts
- El Niño-Southern Oscillation (ENSO): a coupled oceanic/atmospheric seesaw that occurs in the equatorial Pacific but often has global climatic consequences
- modes: phases of a climatic seesaw; for example, El Niño is the warm mode of the ENSO seesaw, whereas La Niña is the cold mode
- North Atlantic Oscillation (NAO): a seesaw in pressure between the Azores and southwestern Iceland, thought by some scientists to be an expression of the Northern Annular Mode
- Northern annular mode (NAM) and Southern Annular Mode (SAM): also called, respectively, the Arctic and Antarctic Oscillations, seesaws in pressure between the latitudes near 45° northern (or southern) latitude and the North (or South) Pole
- Pacific Decadal Oscillation (PDO): a temperature, pressure, and wind seesaw in the Pacific Ocean
- Pacific-North American (PNA) pattern: a seesaw between northern Pacific and North American pressures
- regimes: another word for “modes,” fitting in with meteorological metaphors such as “fronts”
- seesaw: a change in opposite directions, such as high pressure in one region and low pressure in the other
- teleconnection: a connection between two widely separated regions of the planet that have highly correlated changes in some climatic parameter, usually resulting from a seesaw
Bibliography
Alley, R. B., et al. Abrupt Climate Change: Inevitable Surprises. Washington, D.C.: National Academy Press, 2002.
Collier, Michael, and Robert H. Webb. Floods, Droughts, and Climate Change. Tucson: University of Arizona Press, 2002.
Hobeichi, Sanaa. "How Well Do Climate Modes Explain Precipitation Variability?" NPJ Climate and Atmospheric Science, 4 Dec, 2024, doi.org/10.1038/s41612-024-00853-5. Accessed 21 Dec. 2024.
McGregor, Glenn. "Modes of Climate Variability." Springer Nature Link, 10 Oct. 2024, doi.org/10.1007/978-3-031-69906-1‗5. Accessed 21 Dec. 2024.
Rohli, Robert V., and Anthony J. Vega. Climatology. Sudbury, Mass.: Jones & Bartlett, 2008.