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Climatic Change

, Volume 51, Issue 2, pp 131–172 | Cite as

Comparison of Agricultural Impacts of Climate Change Calculated from High and Low Resolution Climate Change Scenarios: Part I. The Uncertainty Due to Spatial Scale

  • L. O. Mearns
  • W. Easterling
  • C. Hays
  • D. Marx
Article

Abstract

We investigated the effect of two different spatial scales of climate change scenarios on crop yields simulated by the EPIC crop model for corn, soybean, and wheat, in the central Great Plains of the United States. The effect of climate change alone was investigated in Part I. In Part II (Easterling et al., 2001) we considered the effects ofCO2 fertilization effects and adaptation in addition to climate change. The scenarios were formed from five years of control and 2 ×CO2 runs of a high resolution regional climate model (RegCM) and the same from an Australian coarse resolution general circulation model (GCM), which provided the initial and lateral boundary conditions for the regional model runs. We also investigated the effect of two different spatial resolutions of soil input parameters to the crop models. We found that for corn and soybean in the eastern part of the study area, significantly different mean yield changes were calculated depending on the scenario used. Changes in simulated dryland wheat yields in the western areas were very similar, regardless of the scale of the scenario. The spatial scale of soils had a strong effect on the spatial variance and pattern of yields across the study area, but less effect on the mean aggregated yields. We investigated what aspects of the differences in the scenarios were most important for explaining the different simulated yield responses. For instance, precipitation changes in June were most important for corn and soybean in the eastern CSIRO grid boxes. We establish the spatial scale of climate changescenarios as an important uncertainty for climate change impacts analysis.

Keywords

Spatial Scale Regional Climate Model Climate Change Impact Climate Change Scenario Wheat Yield 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Briegleb, B. P.: 1992, 'Delta-Eddington Approximation for Solar Radiation in the NCAR Community Climate Model', J. Geophys. Res. 97, 7603–7612.Google Scholar
  2. Brown, R. A., Rosenberg, N. J., Easterling, W. E., Hays, C., and Mearns, L.O.: 2000, 'Potential Production and Environmental Effects of Switchgrass and Traditional Crops under Current and Greenhouse-Altered Climate in the MINK Region of the Central United States', Agric. Ecosystems Environ. 78, 31–47.Google Scholar
  3. Carter, T. R., Parry, M. L., Harasawa, H., and Nishioka, S.: 1994, IPCC Technical Guidelines for Assessing Climate Change Impacts and Adaptations, WMO/UNEP, p. 59.Google Scholar
  4. Cohen, S. J.: 1990, 'Bringing the GlobalWarming Issue Closer to Home: The Challenge of Regional Impact Studies', Bull. Amer. Meteorol. Soc. 71, 520–526.Google Scholar
  5. Dickinson, R. E., Henderson-Sellers, A., and Kennedy, P. J.: 1993, Biosphere-Atmosphere Transfer Scheme (BATS) Version 1E as Coupled to the NCAR Community Climate Model, NCAR Technical Note.Google Scholar
  6. Easterling, W. E., Chen, X., Hays, C. J., Brandle, J., and Zhang, H.: 1996, 'Improving the Validation of Model-Simulated Crop Yield Response to Climate Change: An Application to the EPIC Model', Clim. Res. 6, 263–273.Google Scholar
  7. Easterling, W. E., Crosson, P. R., Rosenberg, N. J., McKenney, M. S., Katz, L. A., and Lemon, K. M.: 1993, 'Agriculture Impacts of and Responses to Climate Change in the Missouri–Iowa– Nebraska–Kansas (MINK) Region', Clim. Change 24, 23–61.Google Scholar
  8. Easterling, W., Mearns, L. O., Hays, C., and Marx, D.: 2001, 'Comparison of Agricultural Impacts of Climate Change Calculated from High and Low Resolution Climate Model Scenarios: Part II. Accounting for Adaptation and CO2 Direct Effects', Clim. Change 51, 173–197.Google Scholar
  9. Easterling, W. E., Rosenberg, N. J., McKenney, M. S., Jones, C. A., Dyke, P. T., and Williams, J. R.: 1992, 'Preparing the Erosion Productivity Impact Calculator (EPIC) Model to Simulate Crop Response to Climate Change and the Direct Effects of CO2', Agric. For. Meteorol. 59, 17–24.Google Scholar
  10. Easterling, W., Weiss, A., Hays, C., and Mearns, L.: 1998, 'Optimum Spatial Scales of Climate Information for Simulating the Effects of Climate Change on Agrosystem Productivity: The Case of the U.S. Great Plains', J. Agric. For. Meteorol. 90, 51–63.Google Scholar
  11. Gates, W. L.: 1985, 'The Use of General Circulation Models in the Analysis of the Ecosystem Impacts of Climatic Change', Clim. Change 7, 267–284.Google Scholar
  12. Giorgi, F. and Hewitson, B.: 2001, Regional Climate Simulation – Evaluation and Projection, IPCC Third Assessment Report, Working Group 1, Chapter 10, Cambridge University Press, Cambridge, pp. 583–638.Google Scholar
  13. Giorgi, F. and Marinucci, R.: 1996, 'A Study of the Sensitivity of Simulated Precipitation to Model Resolution and Its Implications for Climate Studies', Mon. Wea. Rev. 124, 148–166.Google Scholar
  14. Giorgi, F., Marinucci, M. R., and Bates, G. T.: 1993a, 'Development of a Second Generation Regional Climate Model (RegCM2): Boundary Layer and Radiative Transfer Processes', Mon. Wea. Rev. 121, 2794–2813.Google Scholar
  15. Giorgi, F., Marinucci, M. R., De Canio, G., and Bates, G. T.: 1993b, 'Development of a Second Generation Regional Climate Model (RegCM2): Convective Processes and Assimilation of Lateral Boundary Conditions', Mon. Wea. Rev. 121, 2814–2832.Google Scholar
  16. Giorgi, F. and Mearns, L. O.: 1991, 'Approaches to the Simulation of Regional Climate Change: A Review', Rev. Geophys. 29, 191–216.Google Scholar
  17. Giorgi, F., Mearns, L., Shields, S., and McDaniel, L.: 1998, 'Regional Nested Model Simulations of Present Day and 2 ?CO2 climate over the Central Great Plains of the United States', Clim. Change 40, 457–493.Google Scholar
  18. Grell, G. A., Dudhia, J., and Stauffer, D. R.: 1994, A Description of the Fifth Generation Penn State/NCAR Mesoscale Model (MM5), NCAR Technical Note, NCAR/TN-398+STR, National Center for Atmospheric Research, Boulder, CO.Google Scholar
  19. Haas, T. C.: 1995, 'Local Prediction of a Spatio-Temporal Process with an Application to Wet Sulphate Deposition', J. Amer. Stat. Assoc. 90, 1189–1199.Google Scholar
  20. Hassell and Associates: 1998, Climate Change Scenarios and Managing the Scarce Water Resources of the Macquarie River, Australian Greenhouse Office, Canberra.Google Scholar
  21. Holtslag, A. A. M., de Bruijn, E. I. F., and Pan, H. L.: 1990, 'A High Resolution Air Transformation Model for Short-Range Weather Forecasting', Mon. Wea. Rev. 118, 1561–1575.Google Scholar
  22. Hulme, M., Barrow, E. M., Arnell, N., Harrison, P. A., Downing, T. E., and Johns, T. C. J.: 1999, 'Relative Impacts of Human-Induced Climate Change and Natural Climate Variability', Nature 397, 688–691.Google Scholar
  23. Kiniry, J. R., Spanel, D. A., Williams, J. R., and Jones, C. A.: 1990, Demonstration and Validation of Crop Grain Yield Simulation by EPIC. EPIC-Erosion Productivity Impact Calculator. 1. Model Documentation, USDA-ARS Tech. Bull. No. 1768, pp. 220–234.Google Scholar
  24. Lamb, P. J.: 1987, 'On the Development of Regional Climatic Scenarios for Policy-Oriented Climatic-Impact Assessment', Bull. Amer. Meteorol. Soc. 68, 1116–1123.Google Scholar
  25. Mearns, L. O.: 2000, 'Climatic Change and Variability', in Reddy, K. R. and Hodges, H. (eds.), Climate Change and Global Crop Productivity, Chap. 2, CAB, Melbourne, pp. 7–29.Google Scholar
  26. Mearns, L. O., Carbone, G., Gao, W., McDaniel, L., Tsvetsinskaya, E., McCarl, B., and Adams, R.: 2000a, 'The Issue of Spatial Scale in Integrated Assessments: An Example of Agriculture in the Southeastern U.S.', in Preprints of the 80th AMS Annual Meeting, 11th Symposium on Global Change Studies, January 9–14, 2000, Long Beach, CA, American Meteorological Society, Boston, pp. 38–41.Google Scholar
  27. Mearns, L. O., Carbone, G., Gao, W., McDaniel, L., Tsevtsinskaya, E., McCarl, B., Adams, R., and Easterling, W.: 2000b, 'The Importance of Spatial Scale of Climate Scenarios for Regional Climate Change Impacts Analysis: Implications for Regional Climate Modeling Activities', in Preprints of the Tenth PSU/NCAR Mesoscale User's Workshop, 21–22 June 2000, Boulder CO, Mesoscale and Microscale Division, National Center for Atmospheric Research, Boulder.Google Scholar
  28. Mearns, L. O., Easterling, W., and Hays, C.: 1998, 'The Effect of Spatial Scale of Climate Change Scenarios on the Determination of Impacts: An Example of Agricultural Impacts on the Great Plains', Proceedings of the International Workshop on Regional Modeling of the General Monsoon System in Asia, Beijing, 20–23 October, Beijing: START Regional Center for Temperate East Asia, TEACOM Report No. 4, pp. 70–73.Google Scholar
  29. Mearns, L. O., Giorgi, F., McDaniel, L., and Brodeur, C.: 1995b, 'Analysis of the Diurnal Range and Variability of Daily Temperature in a Nested Modeling Experiment: Comparison with Observations and 2×CO2 Results', Clim. Dyn. 11, 193–209.Google Scholar
  30. 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', Glob. Plan. Change 10, 55–78.Google Scholar
  31. Mearns, L. O. and Hulme, M.: 2001, 'Climate Change Scenario Development', in Intergovernmental Panel on Climate Change (IPCC): IPCC Third Assessment Report, Working Group 1, Chapter 13, Cambridge University Press, Cambridge, pp. 739–768.Google Scholar
  32. Mearns, L. O., Mavromatis, T., Tsvetsinskaya, E., Hays, C., and Easterling, W.: 1999, 'Comparative Responses of EPIC and CERES Crop Models to High and Low Resolution Climate Change Scenarios', special issue of J. Geophys. Res. on New Developments and Applications with the NCAR Regional Climate Model (RegCM) 104, 6623–6646.Google Scholar
  33. Mearns, L. O., Rosenzweig, C., and Goldberg, R.: 1997, 'Mean and Variance Change in Climate Scenarios: Methods, Agricultural Applications, and Measures of Uncertainty', Clim. Change 35, 367–396.Google Scholar
  34. Montgomery, D. C. and Peck, E. A.: 1982, Introduction to Linear Regression Analysis, John Wiley and Sons, New York, p. 279.Google Scholar
  35. Parry, M. and Carter, T.: 1998, Climate Impacts and Adaptation, Earthscan Publications, LTD, London.Google Scholar
  36. Richardson, C. W.: 1981, 'Stochastic Simulation of Daily Precipitation, Temperature, and Solar Radiation', Water Resour. Res. 17, 182–190.Google Scholar
  37. Richardson, C. W.: 1982, A Wind Simulation Model for Wind Erosion Estimation, ASAE Paper No. 82-2576, ASAE, St. Joseph, MI 40985.Google Scholar
  38. Risbey, J. S. and Stone, P. H.: 1996, 'A Case Study of the Adequacy of GCM Simulations for Input to Regional Climate Change Assessments', J. Climate 9, 1441–1467.Google Scholar
  39. Robinson, P. J. and Finkelstein, P. L.: 1989, Strategies for Development of Climate Scenarios, Final Report to the U.S. Environmental Protection Agency, Atmosphere Research and Exposure Assessment Laboratory, Office of Research and Development, USEPA, Research Triangle Park, NC, p. 73.Google Scholar
  40. 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
  41. Rosenberg, N. J., McKenney, M. S., Easterling, W. E., and Lemon, K. M.: 1992, 'Validation of EPIC Model Simulations of Crop Responses to Current Climate and CO2 Conditions: Comparisons with Census, Expert Judgement and Experimental Plot Data', Agric. For. Meteorol. 59, 35–51.Google Scholar
  42. Rosenzweig, C. and Parry, M. L.: 1994, 'Potential Impact of Climate Change onWorld Food Supply', Nature 367, 133–137.Google Scholar
  43. 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
  44. 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
  45. Stone, M. C., Hotchkiss, R. H., Hubbard, C. M., Fontaine, T. A., Mearns, L. O., and Arnold, J. G.: 2001, 'Impacts of Climate Change on the Water Yield of the Missouri Basin', J. AWRA, in press.Google Scholar
  46. USDA: 1994, State Soil Geographic (STATSGO) Data Base Data Use Information, U.S. Dept. of Agric., Soil Conser. Serv., National Soil Survey Center. Misc. Pub. Mo. 1392.Google Scholar
  47. Watterson, I. G., Dix, M. R., Gordon, H. B., and McGregor, J. L.: 1995, 'The CSIRO Nine-Level Atmospheric General CirculationModel and Its Equilibrium Present and Doubled CO2 Climate', Aust. Met. Mag. 44, 111–125.Google Scholar
  48. Whetton, P. H., Pittock, A. B., Haylock, M. R., and Rayner, P. J.: 1994, 'An Assessment of Possible Climate Change in the Australian Region Based on an Intercomparison of General Circulation Modeling Results', J. Climate 7, 441–463.Google Scholar
  49. Whetton, P. and Pittock, A. B.: 1991, Australian Region Intercomparison of the Results of some Greenhouse General Circulation Experiments, Tech. Paper No. 21, CSIRO Div. of Atmospheric Research, Melbourne, Australia, p. 73.Google Scholar
  50. Williams, J. R., Jones, C. A., and Dyke, P. T.: 1984, 'A Modeling Approach to Determining the Relationship between Erosion and Soil Productivity', Trans. ASAE 27, 129–144.Google Scholar
  51. Williams, J. R., Jones, C. A., and Dyke, P. T.: 1990, The EPIC Model. EPIC-Erosion/Productivity Impact Calculator. 1. Model Documentation, USDA-ARS Tech. Bull. No. 1768, pp. 3–92.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • L. O. Mearns
    • 1
  • W. Easterling
    • 2
  • C. Hays
    • 3
  • D. Marx
    • 3
  1. 1.National Center for Atmospheric ResearchBoulderU.S.A
  2. 2.Pennsylvania State UniversityCollege ParkU.S.A
  3. 3.University of NebraskaLincolnU.S.A

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