Skip to main content
Log in

MEAN AND VARIANCE CHANGE IN CLIMATE SCENARIOS: METHODS, AGRICULTURAL APPLICATIONS, AND MEASURES OF UNCERTAINTY

  • Published:
Climatic Change Aims and scope Submit manuscript

Abstract

Our central goal is to determine the importance of including both mean and variability changes in climate change scenarios in an agricultural context. By adapting and applying a stochastic weather generator, we first tested the sensitivity of the CERES-Wheat model to combinations of mean and variability changes of temperature and precipitation for two locations in Kansas. With a 2°C increase in temperature with daily (and interannual) variance doubled, yields were further reduced compared to the mean only change. In contrast, the negative effects of the mean temperature increase were greatly ameliorated by variance decreased by one-half. Changes for precipitation are more complex, since change in variability naturally attends change in mean, and constraining the stochastic generator to mean change only is highly artificial. The crop model is sensitive to precipitation variance increases with increased mean and variance decreases with decreased mean. With increased mean precipitation and a further increase in variability Topeka (where wheat cropping is not very moisture limited) experiences decrease in yield after an initial increase from the 'mean change only’ case. At Goodland Kansas, a moisture-limited site where summer fallowing is practiced, yields are decreased with decreased precipitation, but are further decreased when variability is further reduced. The range of mean and variability changes to which the crop model is sensitive are within the range of changes found in regional climate modeling (RegCM) experiments for a CO2 doubling (compared to a control run experiment). We then formed two types of climate change scenarios based on the changes in climate found in the control and doubled CO2 experiments over the conterminous U. S. of RegCM: (1) one using only mean monthly changes in temperature, precipitation, and solar radiation; and (2) another that included these mean changes plus changes in daily (and interannual) variability. The scenarios were then applied to the CERES-Wheat model at four locations (Goodland, Topeka, Des Moines, Spokane) in the United States. Contrasting model responses to the two scenarios were found at three of the four sites. At Goodland, and Des Moines mean climate change increased mean yields and decreased yield variability, but the mean plus variance climate change reduced yields to levels closer to their base (unchanged) condition. At Spokane mean climate change increased yields, which were somewhat further increased with climate variability change. Three key aspects that contribute to crop response are identified: the marginality of the current climate for crop growth, the relative size of the mean and variance changes, and timing of these changes. Indices for quantifying uncertainty in the impact assessment were developed based on the nature of the climate scenario formed, and the magnitude of difference between model and observed values of relevant climate variables.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Adams, R. M., Bryant, K. J., McCarl, B. A., Legler, D. M., O'Brian, J., Solow, A., and Weiher, R.: 1995, 'Value of Improved Long-Range Weather Information’, Contemp. Econ. Pol. 13, 10–19.

    Google Scholar 

  • Anderson, J. R. and Hazell, P. B. R. (eds.): 1989, Variability in Grain Yields, Johns Hopkins, Baltimore, p. 395.

    Google Scholar 

  • Barrow, E. and Semenov, M. A.: 1995, 'Climate Change Scenarios with High Spatial and Temporal Resolution for Agricultural Applications’, Forestry 68, 349–360.

    Google Scholar 

  • Cane, M. A., Eshel, G., and Buckland, R. W.: 1994, 'Forecasting Zimbabwean Maize Yield Using Eastern Equatorial Pacific Sea Surface Temperature’, Nature 370, 204–205.

    Google Scholar 

  • Carter, T. R. and Parry, M. L.: 1986, 'Climatic Changes and Yield Variability’, in Hazell, P. B. R. (ed.), Summary Proceedings of a Workshop on Cereal Yield Variability, International Food Policy Research Institute, Washington, D.C., pp. 47–68.

    Google Scholar 

  • Cohen, S. J.: 1990, 'Bringing the Global Warming Issue Closer to Home: The Challenge of Regional Impact Studies’, Bull. Am. Met. Soc. 71, 520–526.

    Google Scholar 

  • Cooter, E. J.: 1990, 'The Impact of Climate Change on Continuous Corn Production in the Southern U. S. A.’, Climatic Change 16, 53–82.

    Google Scholar 

  • Gibbons, J. D.: 1985, Nonparametric Statistical Inference, Marcel Dekker, Inc., New York, 408 pp.

    Google Scholar 

  • Giorgi, F., and Mearns, L. O.: 1991, 'Approaches to the Simulation of Regional Climate Change: A Review’, Rev. of Geophys. 29, 191–216.

    Google Scholar 

  • Giorgi, F., Shields Brodeur, C., and Bates, G. T.: 1994, 'Regional Climate Change Scenarios over the United States Produced with a Nested Regional Climate Model: Spatial and Seasonal Characteristics’, J. Clim. 7, 375–399.

    Google Scholar 

  • Hodges, Tom: 1991, Predicting Crop Phenology, CRC Press, Boca Raton, p. 233.

    Google Scholar 

  • Houghton, J. T., Jenkins, G. T., and Ephraums, J. J. (eds.): 1990, Climate Change: The IPCC Scientific Assessment, Report Prepared for the IPCC Working Group I, University Press, Cambridge, p. 365.

    Google Scholar 

  • Houghton, J. T., Callander, B. A., and Varney, S. K. (eds.): 1992, Climate Change 1992. The Supplementary Report to the IPCC Scientific Assessment, University Press, Cambridge.

    Google Scholar 

  • Houghton, J. T., Filho, L. G. M., Callander, B. A., Harris, N., Kattenberg, A., and Maskell, K. (eds.): 1996, Climate Change 1995: The Science of Climate Change, Report Prepared for the IPCC Working Group I, University Press, Cambridge.

    Google Scholar 

  • Johnson, G. L., Hanson, C. L., and Ballard, E. B.: 1996, 'Stochastic Weather Simulation: Overview and Analysis of Two Commonly Used Models’, J. Appl. Meteor. 35, 1878–1896.

    Google Scholar 

  • Kaiser, H. M., Riha, S. J., Wilks, D. S., and Sampath, R.: 1993, 'A Farm-Level Analysis of Economic and Agronomic Impacts of Gradual Warming’, Am. J. Agric. Econ. 75, 387–398.

    Google Scholar 

  • Katz, R. W.: 1996: 'The Use of Stochastic Models to Generate Climate Change Scenarios’, Clim. Change 32, 237–255.

    Google Scholar 

  • Katz, R. W. and Brown, B. G.: 1992, 'Extreme Events in a Changing Climate: Variability is More Important than Averages’, Clim. Change 21, 289–302.

    Google Scholar 

  • Lettenmaier, D.: 1995, 'Stochastic Modeling of Precipitation with Applications to Climate Modeling Downscaling’, in von Storch, H. and Navarra, A. (eds.), Analysis of Climate Variability: Applications of Statistical Techniques, Springer-Verlag, Berlin, Chap. 11, pp. 197–212.

    Google Scholar 

  • Mearns, L. O.: 1995, 'Research Issues in Determining the Effects of Changing Climatic Variability on Crop Yields’, in Rosenzweig, C. (ed.), Climate Change and Agriculture: Analysis of Potential International Impacts, American Society of Agronomy Special Publication No. 59, Madison, ASA, Chap. 6, pp. 123–146.

    Google Scholar 

  • Mearns, L. O., Katz, R. W., and Schneider, S. H.: 1984, 'Extreme High-Temperature Events: Changes in Their Probabilities with Changes in Mean Temperature’, J. Clim. Appl. Meteor. 23, 1601–1613.

    Google Scholar 

  • Mearns, L. O. and Rosenzweig, C.: 1994, 'Use of a Nested Regional Climate Model with Changed Daily Variability of Precipitation and Temperature to Test Related Sensitivity of Dynamic Crop Models’, in Preprints of the AMS Fifth Symposium on Global Change Studies, January 23–28, 1994, American Meteorological Society, Boston, pp. 142–155.

    Google Scholar 

  • Mearns, L. O. and Rosenzweig, C.: 1997, 'Formation of Climate Change Scenarios Incorporating Changes in Daily Climate Variability and Application to Crop Models, in Howe, W. and Henderson-Sellers, A. (eds.), Assessing Climate Change: The Story of the Model Evaluation Consortium for Climate Assessment, Chap. 15, Harwood Academic Publishers (in press).

  • Mearns, L. O., Rosenzweig, C., and Goldberg, R.: 1992, 'The Effect of Changes in Interannual Climatic Variability on CERES-Wheat Yields: Sensitivity and 2 × CO2 studies’, J. Agric. Forest Meteorol. 62, 159–189.

    Google Scholar 

  • Mearns, L. O., Rosenzweig, C., and Goldberg, R.: 1996, 'The Effect of Changes in Daily and Interannual Climatic Variability on CERES-Wheat: A Sensitivity Study’, Clim. Change 32, 257–292.

    Google Scholar 

  • Mearns, L. O., Giorgi, F., Shields Brodeur, C., and McDaniel, L.: 1995a, 'Analysis of the Variability of Daily Precipitation in a Nested Modeling Experiment: Comparison with Observations and 2 × CO2 Results’, Global and Planetary Change 10, 55–78.

    Google Scholar 

  • Mearns, L. O., Giorgi, F., Shields, C., and McDaniel, L.: 1995b, 'Analysis of the Diurnal Range and Variability of Daily Temperature in a Nested Modeling Experiment: Comparison with Observations and 2 × CO2 Results’, Clim. Dynam. 11, 193–209.

    Google Scholar 

  • Mendelsohn, R., Nordhaus, W. D., and Shaw, D.: 1994, 'The Impact of Global Warming on Agriculture: A Ricardian Analysis’, Am. Econ. Rev. 84, 753–771.

    Google Scholar 

  • Otter-Nacke, S., Goodwin, D. C., and Ritchie, J. T.: 1986, Testing and Validating the CERES-Wheat Model in Diverse Environments, AGRISTARS YM-15-00407 JSC 20244, Johnson Space Center, Houston.

    Google Scholar 

  • Parry, M. L. and Carter, T. R.: 1985, 'The Effect of Climatic Variations on Agricultural Risk’, Clim. Change 7, 95–110.

    Google Scholar 

  • Priestley, C. H. B. and Taylor, R. J.: 1972, 'On the Assessment of Surface Heat and Evaporation Using Large-Scale Parameters’, Mon. Wea. Rev. 100, 81.

    Google Scholar 

  • Reilly, J. (convening author): 1996, 'Agriculture in a Changing Climate: Impacts and Adaptations’, in Intergovernmental Panel on Climate Change (IPCC), Climate Change 1995. Impacts, Adaptations and Mitigation of Climate Change, Contribution of Working Group II to the Second Assessment Report of the IPCC, Cambridge Univ. Press, Cambridge, Chap. 13, pp. 427–467.

    Google Scholar 

  • Riha, S. J., Wilks, D. S., and Simoens, P.: 1996, 'Impact of Temperature and Precipitation Variability on Crop Model Predictions’, Clim. Change 32, 293–311.

    Google Scholar 

  • Ritchie, J. T. and Otter, S.: 1985, 'Description and Performance of CERES-Wheat: A User-Oriented Wheat Yield Model’, in Willis, W. O. (ed.), ARS Wheat Yield Project, ARS-38, U.S. Department of Agriculture-Agricultural Research Service, pp. 159–175.

  • Richardson, C. W.: 1981, 'Stochastic Simulation of Daily Precipitation, Temperature, and Solar Radiation’, Water Res. Research 17, 182–190.

    Google Scholar 

  • Richardson, C. W. and Wright, D. A.: 1984, WGEN: A Model for Generating Daily Weather Variables, ARS-8, U. S. Dept. of Agriculture, Agricultural Research Service, Washington, D.C.

    Google Scholar 

  • Robock, A., Turco, R. P., Harwell, M. A., Ackerman, T. P., Andressen, R., Chang, H.-S., and Sivakumar, M. V. K.: 1993: 'Use of General Circulation Model Output in the Creation of Climate Change Scenarios for Impact Analysis’, Clim. Change 23, 293–355.

    Google Scholar 

  • Rosenzweig, C.: 1990, 'Crop Response to Climate Change in the Southern Great Plains: A Simulation Study’, The Professional Geographer 42, 20–39.

    Google Scholar 

  • Rosenzweig, C.: 1994, 'Maize Suffers a Sea Change’, Nature 370, 175–176.

    Google Scholar 

  • Rosenzweig, C. and Parry, M. L.: 1994, 'Potential Impact of Climate Change on World Food Supply’, Nature 367, 133–137. on

    Google Scholar 

  • Semenov, M. A. and Porter, J. R.: 1995, 'Climatic Variability and Modelling of Crop Yields’, Agric. Forest Meteorol. 73, 265–283.

    Google Scholar 

  • Semenov, M. A. and Barrow, E.: 1997, 'Use of a Stochastic Weather Generator in the Development of Climate Change Scenarios’, Clim. Change 35, 397–414.

    Google Scholar 

  • Smith, J. B. and Tirpak, D. A. (eds.): 1989, The Potential Effects of Global Climate Change on the United States, U. S. EPA. Report to Congress No. 230-05-61-050, U.S. Environmental Protection Agency, Washington, D.C.

    Google Scholar 

  • Thompson, S. L. and Pollard, D.: 1995a, 'A Global Climate Model (GENESIS) with a Land-Surface-Transfer Scheme (LSX). Part I: Present Climate Simulation’, J. Clim. 8, 732–761.

    Google Scholar 

  • Thompson, S. L. and Pollard, D.: 1995b, 'A Global Climate Model (GENESIS) with a Land-Surface-Transfer Scheme (LSX). Part II. CO2 Sensitivity’, J. Clim. 8, 1104–1121.

    Google Scholar 

  • Waggoner, P. E.: 1989, 'Anticipating the Frequency Distribution of Precipitation if Climate Change Alters Its Mean’, J. Ag. For. Meteorol. 47, 321–337.

    Google Scholar 

  • Wang, Y. P. and Conner, D. J.: 1996, 'Simulation of Optimal Development for Spring Wheat at Two Locations in Southern Australia under Present and Changed Climate Conditions’, Agric. For. Meteor. 79, 9–28.

    Google Scholar 

  • Wilks, D. S.: 1989, 'Conditioning Stochastic Daily Precipitation Models on Total Monthly Precipitation’, Water Res. Res. 25, 1429–1439.

    Google Scholar 

  • Wilks, D. S.: 1992, 'Adapting Stochastic Weather Generation Algorithms for Climate Change Studies’, Clim. Change 22, 67–84.

    Google Scholar 

  • Williams, J. R., Dyke, P. T., and Jones, C. A.: 1982, 'EPIC — A Model for Assessing the Effects of Erosion on Soil Productivity’, Proc. Int. Conf. on State-of-the-Art in Ecol. Modelling, Colorado State U., May 24–28, 1982.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

MEARNS, L.O., ROSENZWEIG, C. & GOLDBERG, R. MEAN AND VARIANCE CHANGE IN CLIMATE SCENARIOS: METHODS, AGRICULTURAL APPLICATIONS, AND MEASURES OF UNCERTAINTY. Climatic Change 35, 367–396 (1997). https://doi.org/10.1023/A:1005358130291

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1005358130291

Keywords

Navigation