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Climate sensitivity and climate state - Climate Dynamics

  • ️Yu, B.
  • ️Tue Apr 15 2003

Abstract

The effective climate feedback/sensitivity, including its components, is a robust first order feature of the Canadian Centre for Climate Modelling and Analysis (CCCma) coupled global climate model (GCM) and presumably of the climate system. Feedback/sensitivity characterizes the surface air temperature response to changes in radiative forcing and is constant, to first order, independent of the nature, history, and magnitude of the forcing and of the changing climate state. This "constancy" can only be approximate, however, and modest second order changes of 10–20% are found in stabilization simulations in which the forcing, based on the IS92a scenario, is fixed (stabilized) at year 2050 and 2100 values and the system is integrated for an additional 1000 years toward a new equilibrium. Both positive and negative feedback mechanisms tend to strengthen, with the balance tilted toward stronger negative feedback and hence weaker climate sensitivity, as the system evolves and warms. Some feedback mechanisms weaken locally, however, and an example of such is the ice/snow albedo feedback which is less effective in areas of the Northern Hemisphere where ice/snow has retreated. Changes in the geographical distribution of the feedbacks are modest and weakening feedback in one region is often counteracted by strengthening feedback in other regions so that global and zonal values do not reflect the dominance of a particular mechanism or region but rather the residual of changes in different components and regions. The overall 10–20% strengthening of the negative feedback (decrease in climate sensitivity) in the CCCma model contrasts with a weakening of negative feedback (increase in climate sensitivity) of over 20% in the Hadley Centre model under similar conditions. The different behaviour in the two models is due primarily to solar cloud feedback with a strengthening of the negative solar cloud feedback in the CCCma model contrasting with a weakening of it in the Hadley Centre model. The importance of processes which determine cloud properties and distribution is again manifest both in determining first order climate feedback/sensitivity and also in determining its second order variation with climate state.

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Acknowledgements.

We greatly appreciate the work of Greg Flato, Dave Ramsden, Cathy Reader, Warren Lee, and other members of CCCma in the production of the CGCM results analyzed here and of B. Pal in processing some of the results. D. Robataille reran part of the simulations to obtain the cloud forcing results. We thank Steve Lambert and Vivek Arora for helpful comments.

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Authors and Affiliations

  1. Canadian Centre for Climate Modelling and Analysis, Meteorological Service of Canada, University of Victoria, Canada, PO Box 1700, Victoria, B.C. V8W 2Y2

    G. J. Boer & B. Yu

Authors

  1. G. J. Boer

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  2. B. Yu

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Correspondence to G. J. Boer.

Appendix 1

Appendix 1

1.1 Sensitivity of feedback calculations to estimates of radiative forcing

We consider the stabilization calculations leading to Fig. 2. From Eq. (4) the global feedback may be expressed as \(\hat \Lambda _\alpha = {{\langle \hbox{d}h^{\prime}_\alpha /\hbox{d}t\rangle - \langle f\rangle} \over {\langle T^{\prime}_\alpha \rangle}} \to {{- \langle f_{g_\alpha} + f_a \rangle} \over {\langle T^{\prime}_\alpha \rangle}}\) at equilibrium where α = 1, 2 identifies the S2050 and S2100 simulations respectively. The stabilized GHG forcing f g differs considerably between the two cases but the aerosol forcing f a is essentially the same (the aerosol loading changes very little after 2050). The overall forcing f in each case does not change after stabilization but there may be some error in our estimate of it. We ask to what extent we would have to modify either the GHG or aerosol forcing so that the S2050 and S2100 feedbacks attain the same value (i.e. so that Λ1 = Λ2) some 80 decades after stabilization in Fig. 2.

First presume that f g is correct, and consider the modification to f a that would be required to make the feedbacks agree. We replace f a by (1 + ɛ)f a , set the resulting "corrected" expressions for feedback equal to one another \(\hat \Lambda _1 - \varepsilon \langle f_a \rangle /\langle T^{\prime}_1 \rangle = \hat \Lambda _2 - \varepsilon \langle f_a \rangle /\langle T^{\prime}_2 \rangle \) and solve for ɛ with the appropriate values of the quantities in Figs. 1 and 2. The result gives ɛ ≈ –0.40 so that 〈f a 〉 would have to be decreased to 60% of its estimated value to cause the feedback values to agree at equilibrium. Similarly, assuming that 〈f a 〉 is correct and modifying 〈f g〉 gives ɛ ≈ 0.40 so that the 〈f g 〉 would have to be increased to 140% of their estimated values to obtain agreement of the Λs. Errors of this magnitude are considered to be unlikely.

Finally, applying the same approach to errors in both forcings and requiring the same (and hence the minimum) magnitude (but not sign) of the fractional error ɛ for both forcings gives |ɛ| ≈ 0.20 requiring a decrease in aerosol forcing to about 80% of its value combined with an increase of GHG forcing to 120% of it value. There is no apparent reason why aerosol forcing should be underestimated while GHG forcing is overestimated by this amount and we conclude that the modest differences between the feedbacks for the S2050 and S2100 cases are likely to be real.

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Boer, G.J., Yu, B. Climate sensitivity and climate state. Climate Dynamics 21, 167–176 (2003). https://doi.org/10.1007/s00382-003-0323-7

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  • Received: 29 August 2002

  • Accepted: 09 January 2003

  • Published: 15 April 2003

  • Issue Date: August 2003

  • DOI: https://doi.org/10.1007/s00382-003-0323-7

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