Systematic Analysis of Climatic Model Sensitivity to Parameters and Processes

  1. James E. Hansen and
  2. Taro Takahashi
  1. Matthew C. G. Hall and
  2. Dan G. Cacuci

Published Online: 19 MAR 2013

DOI: 10.1029/GM029p0171

Climate Processes and Climate Sensitivity

Climate Processes and Climate Sensitivity

How to Cite

Hall, M. C. G. and Cacuci, D. G. (1984) Systematic Analysis of Climatic Model Sensitivity to Parameters and Processes, in Climate Processes and Climate Sensitivity (eds J. E. Hansen and T. Takahashi), American Geophysical Union, Washington, D. C.. doi: 10.1029/GM029p0171

Author Information

  1. Engineering Physics Division, Oak Ridge National Laboratory, Oak Ridge, Tennesse 37830

Publication History

  1. Published Online: 19 MAR 2013
  2. Published Print: 1 JAN 1984

ISBN Information

Print ISBN: 9780875904047

Online ISBN: 9781118666036

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Keywords:

  • Climatology—Congresses;
  • Geophysics—Congresses;
  • Ocean-atmosphere interaction—Congresses

Summary

This paper demonstrates the adjoint method of sensitivity analysis on a radiative-convective climate model with a diurnal cycle. A single adjoint calculation, which requires about the same computation time as the original model, suffices to calculate sensitivities of average surface air temperature to all 312 model parameters. The sensitivities accurately predict the effect on average surface air temperature of small variations in the model parameters. Relative sensitivities rank the importance of all the parameters, the most important parameters being those that characterize solar radiation and determine transmission functions. The uncertainties in the model results are expressed formally in terms of all the sensitivities and parameter covariances. For this model, a 10% standard deviation in surface albedo causes the increase in average surface air temperature after doubling CO2 to have a standard deviation of 0.24 K about the expected value of 1.66 K. For results that cannot readily be compared with observation (for example, the results of a CO2 doubling experiment), this method of uncertainty analysis is the only systematic way to estimate the reliability of model results.