Climate prediction and projection

The methodology and databases accumulated over several decades to monitor and predict normal cyclical variations in climate provide a base against which to assess whether, and to what extent, current global warming is anthropogenic and to what extent it may be self-correcting.

Background

Weather is the sum of the atmospheric conditions we experience on a daily basis; climate is what produces those conditions in a given geographical area. Climatology has been described as “geographical meteorology.” The principal purpose of studying a region’s climate is to discover measurable factors that accurately predict, months or years in advance, what the general weather regime will be like in a given area. In making their predictions, climatologists first look at established historical patterns, both recorded in writing and documented through proxies. Increasingly they also investigate whether there are systematic perturbations in established historical patterns, and they also incorporate the effects of events whose probability is low or unknown.

History

Attempts by humans to forecast climate date back several millennia, at least to the days of ancient Babylon, when astrologers used the motions of the Sun, Moon, and planets to predict whether the coming season would be favorable for agriculture. The practice may be even older. Anthropologist Johannes Wilbert recorded a religiously based system of climate prediction among the Warao Indians of Venezuela, a group of primitive Stone Age agriculturalists. The biblical story of Joseph (c. 1800 b.c.e.) relates how Joseph interpreted Pharaoh’s dream of seven lean cattle devouring seven fat cattle as a prediction of impending drought and famine, enabling the Egyptians to prepare in advance.

Climate prediction for agricultural purposes remained a major function of astrology, and later astronomy, from Babylon to the pioneer European astronomer Tycho Brahe in the sixteenth century. Even into the twenty-first century, many successful American farmers followed the Farmers’ Almanac, with its astrologically based recommendations for planting crops. The practice persisted in part because it has some basis in fact: The phases of the Moon, and to a lesser extent the orbits of Jupiter and Saturn, do affect climate.

Interest in a more scientific and systematic approach to climate prediction gained impetus in the late nineteenth century in response to expansion of Europeans into regions that experience more extreme and variable climatic conditions than Europe. The eleventh edition of the Encyclopedia Britannica (1911) divides the globe into climatic zones, describes the variability of each, and discusses evidence for a general warming trend, which the author of the encyclopedia article was inclined to dismiss as unproven.

A decade later, Sir Gilbert Walker began publishing his pioneering work on fluctuations in the Indian monsoons, their relationship to periodic famines, and the correlation between them and oscillation of high- and low-pressure areas between the Indian Ocean and the tropical Pacific. This Southern Oscillation, later shown to be linked to the El Niño phenomenon in the eastern Pacific, is the most important determinant of cyclical global weather patterns.

Building a Global Climate Prediction Network

Climate prediction depends upon having large numbers of accurate measurements of many different variables, which can then be correlated mathematically. Correlation is an uncertain process at best, rarely yielding unequivocal results. For example, all of the complex hurricane-predicting machinery of the United States’ National Oceanic and Atmospheric Administration (NOAA) produced, as of the beginning of June 2009, the prediction that the upcoming August-October Atlantic hurricane season would have a 50 percent chance of being average and a 25 percent chance of being above or below average. Vague predictions such as this are one reason that skeptics such as Marcel Leroux, of the University of Adelaide, can plausibly question that scientists have demonstrated any general global warming effect.

Global meteorological monitoring, coordinated through national weather services and the United Nations, involves a network of satellites capable of measuring physical parameters including surface temperatures, wind speeds, cloud cover, and barometric pressure, at points 50 kilometers apart, at hourly intervals. National weather centers are well apprised, for example, of the exact status of El Niño on any given day and how it has been developing, but unless it has recently exhibited extraordinary features, these data give only a general picture of what the climate will do in affected regions.

The main thrust of global climate prediction was, and to a large extent still is, extreme weather events, including droughts, floods, and cyclonic storms. Predictions impact disaster preparedness and help nations minimize mortality. Knowing in advance that El Niño is likely to produce drought in Australia and South Africa in a given year helps those countries stockpile grain, devote more acreage to drought-resistant crops, and prepare for wildfires. In the United States and elsewhere, projections for hurricane and tornado activity affect insurance policies and land-use decisions. For extreme weather projections, a decadal time frame is sufficient. Beyond that, only a few cyclical phenomena can be projected with yearly accuracy, and the number of unknown variables becomes too large to allow useful prediction.

The need for more effective methods of weather prediction has led to the employment of different technological methods. Artificial Intelligence (AI) is increasingly being incorporated as a tool for weather prediction, particularly for its utility in predictive modeling. AI models can be an effective supplement to physics-based weather models. AI is now used for disaster predictions, planning for surges in energy consumption, and trajectories of storm paths.

In early 2023 the NOAA announced an initiative where it would assist communities in collecting data that would increase its understanding of climate change. This included new data repositories, the launch of two satellites to provide environmental intelligence, and an upgrade in computer processing capabilities. Another advancement has been the employment of the Advanced Dvorak Technique (ADT). This uses satellite-based imagery to assess the strength of phenomena such as tropical cyclones.

Climate Projection

All of the models used in predicting decadal climate variability were developed using historical data and assume that variables are constant, oscillate in a regular manner around a mean, or are increasing or decreasing at a constant rate. With respect to the carbon dioxide content of the atmosphere, none of these conditions is currently met. However, once a model is developed, climatologists can use a computer simulation to project, for example, how atmospheric carbon dioxide and global temperatures would respond if there were a large increase or decrease in emissions. Because of complexities, uncertainties, and unknown variables, such projections often prove to be far off track.

Context

With respect to global warming, input from climatologists associated with NOAA and other agencies often suffers from distortion between laboratory and the media. When scientists correctly project from their models that a massive eruption of the Yellowstone supervolcano such as occurred 200,000 years ago would produce abrupt catastrophic cooling, completely dwarfing any anthropogenic warming, it implies neither that such an eruption is expected in the near future nor that efforts to curtail emissions and environmental degradation are futile in the face of overwhelming nature—yet that is the lesson many people would derive from their findings. The high resolution, global coverage, and international cooperation among climatology centers ensure that no event or trend of significance escapes attention.

Key Concepts

• anthropogenic climate change: changes in overall long-term weather patterns in a region due to human activity
• extreme weather events: natural disasters caused by weather, including floods, hurricanes, drought, and prolonged severe hot and cold spells
• proxies: measurable parameters, correlated with climate, that are preserved in the geologic record (for example, oxygen isotope ratios and fossil pollen)

Bibliography

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“Five Ways NOAA Supported Climate Science and Solutions in 2022." National Oceanic and Atmospheric Administration, 11 Jan. 2023, www.noaa.gov/stories/5-ways-noaa-supported-climate-science-and-solutions-in-2022. Accessed 19 Jan. 2023.

“Lawton, George. "How AI in Weather Prediction can Aid Human Intelligence." TechTarget, 8 Nov. 2022, www.techtarget.com/searchenterpriseai/feature/How-AI-in-weather-prediction-can-aid-human-intelligence. Accessed 19 Jan. 2023.

Leroux, Marcel. Global Warming: Myth or Reality? The Erring Ways of Climatology. New York: Springer, 2005.

Mayewsky, Paul, et al. “Holocene Climate Variability.” Quaternary Research 62 (2004): 243-255.

Saltzman, Barry. Dynamical Paleoclimatology: Generalized Theory of Global Climate Change. San Diego, Calif.: Academic Press, 2002.

Thompson, Russell D., and Allen Perry, eds. Applied Climatology: Principles and Practice. New York: Routledge, 1997.