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Global Climate Change Digest
A Guide to Information on Greenhouse Gases and Ozone Depletion
Published July 1988 through June 1999

FROM VOLUME 1, NUMBER 4, OCTOBER 1988

PROFESSIONAL PUBLICATIONS...
SPECIAL ISSUE: SOCIAL AND CLIMATE FORECASTING

Item #d88oct26

"Forecasting in the Social and Natural Sciences," K.C. Land (Dept. Sociol., Duke Univ., Durham NC 27707), S.H. Schneider, eds., Climatic Change, 11(1/2), Aug.-Oct. 1987.

This issue is a selection of the papers presented at a 1984 conference in Boulder, Colorado on forecasting that are most likely to interest readers of the journal. The essays cover three areas: (1) organizational and political content of applied forecasting, (2) forecasting models and methods, and (3) issues of predictability, the implications of forecast errors, and model construction, linkage and verification. The papers by Somerville and Liverman deal explicitly with climate forecasting. The contents of this special issue are available as a separate volume of the same title edited by Land and Schneider, from D. Reidel Publishing Company, Spuiboulevard 50, POB 17, 3300 AA Dordrecht, Holland.

Item #d88oct27

"Forecasting in the Social and Natural Sciences: An Overview and Analysis of Isomorphisms," K.C. Land (address immed. above), S.H. Schneider, ibid., 7-31.

Identifies and analyzes several points of similarity in the structure and context of forecasting in the social and natural sciences. Among them are the need for simplifying parameters, limitations on predictability in large-scale systems, problems of model linkage, and the social context of forecasts.

Item #d88oct28

"The Social Forecasting Industry," H.L. Smith (Dept. Sociol., Univ. Pennsylvania, Philadelphia PA 19174), ibid., 35-60.

Reports on the collection of individuals and organizations, both for profit and nonprofit, supplying forecasts of social futures in direct exchange for pay. Discusses methodologies extensively including the Delphi method and cross-impact analysis. Explains how the social forecasting industry and its marketplace differ from forecasting in the social sciences proper.

Item #d88oct29

"Forecasts in Urban Transportation Planning: Uses, Methods, and Dilemmas," M. Wachs (Grad. Sch. Architecture & Urban Planning, Univ. Calif., Los Angeles CA 90024), ibid., 61-80.

Reviews forecasting models widely used by transportation consultants and shows how the models are manipulated to promote systems which have been chosen on the basis of political criteria.

Item #d88oct30

"Forecasting Errors: The Importance of the Decision-Making Context," R.L. Dennis (Environ. Sci. Res. Lab., Research Triangle Pk. NC 27711), ibid., 81-96.

Demonstrates the importance of context on forecast errors. Describes the development of an unrealistic transportation planning forecast for Denver, Colorado, noting its influence on air quality planning for Denver.

Item #d88oct31

"The Delphi Technique and Judgmental Forecasting," T.R. Stewart, (NCAR, POB 3000, Boulder CO 80307), ibid., 97-113.

Describes and summarizes the Delphi technique for judgmental forecasting by expert groups and the controversy surrounding its use. A methodological standard for evaluating judgmental forecasts is proposed.

Item #d88oct32

"Econometric Forecasting: A Brief Survey of Current and Future Techniques," C.W.J. Granger (Dept. Econ., Univ. Calif., San Diego, La Jolla CA 92093), R.F. Engle, ibid., 117-140.

Among techniques discussed are various approaches for univariate series, diagnostic testing of alternative specifications using Lagrangian multiplier techniques, vector autoregressive models, co-integration and error-correction models. These techniques are illustrated using a forecast comparison exercise concerning forecasts of monthly electricity demand per customer.

Item #d88oct33

"A Survey of Census Bureau Population Projection Methods," J.F. Long (Population Div., U.S. Census Bureau, Washington DC 20233), D.B. McMillen, ibid., 141-177.

Reviews each of the forecasting traditions in population projections, describes the U.S. Census Bureau's current methods for national and state population projections, and proposes new hybrid approaches. Possible parallels with forecasting in other disciplines are noted throughout the article.

Item #d88oct34

"Forecasting Health Status Changes in an Aging U.S. Population: Assessment of the Current Status and Some Proposals," K.G. Manton (Demographic Studies, Duke Univ., Durham NC 27707), ibid., 179-210.

Recent efforts at forecasting health and morbidity changes in the population and the recent lack of methodological innovation in health forecasting strategies is examined. Describes two forecasting models--stochastic compartmental systems and mixed multivariate continuous state-discrete state process. Shows how these models can be used in a complementary fashion to improve health projections.

Item #d88oct35

"Recent Developments in Technological Forecasting," J.P. Martino (Univ. Dayton, Dayton OH 45469), ibid., 211-235.

Discusses recent developments in methodology and describes important recent work on estimating upper limits to the progress of technologies, and on quantitative measures of multi-attribute technologies. Discusses several issues common to all forecasting applications, as they are dealt with in technological forecasting, such as validation, disasters of forecasting, determinism in forecasting, and some examples of forecasts with practical applications.

Item #d88oct36

"The Predictability of Weather and Climate," R.C.J. Somerville (Scripps Inst. Oceanog., Univ. Calif., San Diego, La Jolla CA 92093), ibid., 239-246.

Comprehensive numerical models of the large-scale circulation of the atmosphere, based on physical principles, are quite skillful at describing the evolving weather up to a few days ahead, but lose predictability beyond that due to the intrinsic instability of the atmosphere. This does not preclude the possibility of seasonal and longer-range forecasts of means and other statistical properties, but we are only beginning to learn what aspects of climate may be predictable, and what theoretical tools and observational data will be required to predict them.

Item #d88oct37

"Errors in Forecasting Social Phenomena," R.A. Berk (Univ. Calif., Santa Barbara, Calif.), T.F. Cooley, ibid., 247-265.

Examines the nature of forecasting errors associated with social phenomena. Introduces the notion of predictive likelihood and concludes that (1) there is a need to separate the problem of parametric estimation and inference from the problem of forecasting, (2) all forecasts linked to decisions require at least an implicit structural model, (3) conventional cost functions have adversely affected the quality of social science forecasts, and (4) there is a need to develop forecasting procedures robust to different kinds of cost functions.

Item #d88oct38

"Forecasting the Impact of Climate on Food Systems: Model Testing and Model Linkage," D.M. Liverman (Dept. Geog., Univ. Wisconsin, Madison WI 53706), ibid., 267-285.

Model testing using sensitivity analysis and validation techniques is illustrated with two models: (1) the YIELD model which simulates the impact of climate on crop yields of several major crops, and (2) the International Futures Simulation model which can be used to simulate the impact of crop yield changes on the world food system. The problems of linking such models are also discussed.