February 28, 2007
GCRIO Program Overview
Our extensive collection of documents.
Archives of the
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
SPECIAL ISSUE: SOCIAL AND CLIMATE FORECASTING
"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.
"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.
"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.
"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.
"Forecasting Errors: The Importance of the Decision-Making Context,"
R.L. Dennis (Environ. Sci. Res. Lab., Research Triangle Pk. NC 27711), ibid.,
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.
"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.
"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.
"A Survey of Census Bureau Population Projection Methods," J.F.
Long (Population Div., U.S. Census Bureau, Washington DC 20233), D.B. McMillen,
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.
"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.
"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.
"The Predictability of Weather and Climate," R.C.J. Somerville
(Scripps Inst. Oceanog., Univ. Calif., San Diego, La Jolla CA 92093), ibid.,
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.
"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.
"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
Guide to Publishers
Index of Abbreviations