Is it possible to make valid climate predictions that go beyond weeks, months, even a year? As most know weather is not easily predictable. UCLA atmospheric scientists report they have now made long-term climate forecasts that are among the best ever — predicting climate up to 16 months in advance, nearly twice the length of time previously achieved by climate scientists. Forecasts of climate are much more general than short-term weather forecasts; they do not predict precise temperatures in specific cities, but they still may have major implications for agriculture, industry and the economy. The study is currently available online in the journal Proceedings of the National Academy of Sciences (PNAS) and will be published in an upcoming print edition of the journal.
Is it possible to make valid climate predictions that go beyond weeks, months, even a year? As most know weather is not easily predictable. UCLA atmospheric scientists report they have now made long-term climate forecasts that are among the best ever — predicting climate up to 16 months in advance, nearly twice the length of time previously achieved by climate scientists. Forecasts of climate are much more general than short-term weather forecasts; they do not predict precise temperatures in specific cities, but they still may have major implications for agriculture, industry and the economy. The study is currently available online in the journal Proceedings of the National Academy of Sciences (PNAS) and will be published in an upcoming print edition of the journal.
!ADVERTISEMENT!
Climate encompasses the statistics of temperature, humidity, atmospheric pressure, wind, rainfall, atmospheric particle count and other meteorological elemental measurements in a given region over long periods. Climate can be contrasted to weather, which is the present condition of these elements and their variations over shorter periods.
Climate change is the variation in global or regional climates over time. It reflects changes in the variability or average state of the atmosphere over time scales ranging from decades to millions of years. These changes can be caused by processes internal to the Earth, external forces (e.g. variations in sunlight intensity) or, more recently, human activities.
Climate models use quantitative methods to simulate the interactions of the atmosphere, oceans, land surface, and ice. They are used for a variety of purposes from study of the dynamics of the climate system to projections of future climate. The most talked-about use of climate models in recent years has been to project temperature changes resulting from increases in atmospheric concentrations of greenhouse gases.
All climate models take account of incoming energy from the sun as short wave electromagnetic radiation, chiefly visible and short-wave (near) infrared, as well as outgoing energy as long wave (far) infrared electromagnetic radiation from the earth. Any imbalance results in a change in temperature.
Long-term climate forecasts could help predict El Niño events more than a year in advance. El Niño is a climate pattern characterized by the warming of equatorial surface waters, which dramatically disrupts weather patterns over much of the globe and strikes as often as every second year, as seldom as every seventh year or somewhere in between.
A major issue addressed by Ghil and his colleagues in the PNAS research is the difficulty of separating natural climate variability from human-induced climate change and how to take natural variability into account when making climate models.
For the study, Ghil and his UCLA colleagues Michael Chekroun and Dmitri Kondrashov of the department of atmospheric and oceanic sciences analyzed sea-surface temperatures globally. To improve their forecasts, they devised a new algorithm based on novel insights about the mathematics of how short-term weather interacts with long-term climate. Weather covers a period of days, while climate covers months and longer.
As is customary in this field, Ghil and his colleagues used five decades of climate data and test predictions retrospectively. For example, they used climate data from 1950 to 1970 to make "forecasts" for January 1971, February 1971 and beyond and see how accurate the predictions were. They reported achieving higher accuracy in their predictions 16 months out than other scientists achieved in half that time.
For further information: http://www.universityofcalifornia.edu/news/article/26249
Photo: http://cses.washington.edu/cig/outreach/workshopfiles/boise2003/index.htm
