Models Fail to Predict ENSO; Malaria Wont Spread;

Models Fail to Predict ENSO

The prediction of the 1997-98 El Nio was hailed as a great success for computer climate models and seemed to validate their usefulness in forecasting future climate change. One article in Science (1998) proclaimed, "Models win big in forecasting El Nio." A study published by the Bulletin of the American Meteorological Society (September 2000) tests this claim.

The study found that, "the current answer to the question posed in this articles title [How much skill was there in forecasting the very strong 1997-98 El Nio?] is that there was essentially no skill in forecasting the very strong 1997-98 El Nio at lead times ranging from 0 to 8 months." Indeed, no models were "able to anticipate even one-half of the actual amplitude of the El Nios peak at medium range (6-11 months) lead." And, "since no models were able to provide useful predictions at the medium and long ranges, there were no models that provided both useful and skillful forecasts for the entirety of the 1997-98 El Nio" [emphasis in original].

The authors are disturbed "that others are using the supposed success in dynamical El Nio forecasting to support other agendas," citing the American Geophysical Unions Position Statement on Climate Change as an example. "The bottom line is that the successes in ENSO forecasting have been overstated (sometimes drastically) and misapplied in other arenas," according to the study. There should be even "less confidence in anthropogenic global warming studies because of the lack of skill in predicting El Nio."

Malaria Wont Spread

One of the predicted consequences of global warming is the northward spread of infectious disease vectors. The ranges of the mosquitoes that carry malaria and yellow and dengue fever, it is claimed, will move northward as temperatures in the cooler northern regions warm up. These predictions are based on computer models that are driven by temperature changes only.

A new study in Science (September 8, 2000) tests these models against real world data for the global spread of malaria and has found them lacking in their ability to make accurate predictions. In other words, these approaches do not give accurate descriptions of the current distribution of global malaria.

According to the study, "The fit of these predictions to the current global malaria situation shows noticeable mismatches in certain places; false predictions of presence (e.g., over the eastern half of the United States) are accounted for by past control measures or by peculiar vector biogeography, whereas false predictions of absence are dismissed as model errors."

The authors of the study take a multivariate approach to modeling the spread of malaria, taking into account various climatic variables including temperature, humidity and rainfall. The new approach, which gives a better representation of the current situation, "predicted remarkably few future changes, even under the most extreme scenarios of climate change," according to the study.

Website for New Climate Oscillation

A new website tracks the Pacific Decadal Oscillation, which "is a long-lived El Nio-like pattern of Pacific climate variability." The difference between the two oscillations is that El Nio persists from 6 to 18 months, whereas the PDO persists for 20 to 30 years.

Moreover, the PDO coincides perfectly with global temperature changes. From 1947-1976 the PDO cool phase coincided with falling global temperatures. From 1977 to the present the warm phase coincided with rising temperatures. See, http://tao.atmos.washington.edu/pdo.

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