Influence of inhomogeneous surface heat capacity on the estimation of radiative response coefficients in a two-zone energy balance model
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Observationally constrained values of the global radiative response coefficient are pivotal to assess the reliability of modeled climate feedbacks. A widely used approach is to measure transient global radiative imbalance related to surface temperature changes. However, in this approach, a potential error in the estimate of radiative response coefficients may arise from surface inhomogeneity in the climate system. We examined this issue theoretically using a simple two-zone energy balance model. Here, we dealt with the potential error by subtracting the prescribed radiative response coefficient from those calculated within the two-zone framework. Each zone was characterized by the different magnitude of the radiative response coefficient and the surface heat capacity, and the dynamical heat transport in the atmosphere between the zones was parameterized as a linear function of the temperature difference between the zones. Then, the model system was forced by randomly generated monthly varying forcing mimicking time-varying forcing like an observation. The repeated simulations showed that inhomogeneous surface heat capacity causes considerable miscalculation (down to −1.4 W m−2 K−1 equivalent to 31.3% of the prescribed value) in the global radiative response coefficient. Also, the dynamical heat transport reduced this miscalculation driven by inhomogeneity of surface heat capacity. Therefore, the estimation of radiative response coefficients using the surface temperature-radiation relation is appropriate for homogeneous surface areas least affected by the exterior.
This work was funded by the Korea Meteorological Administration Research and Development Program under grant KMIPA2015-6110. Y.-S. Choi acknowledges the support of the Jet Propulsion Laboratory, California Institute of Technology, sponsored by the National Aeronautics and Space Administration (NASA).
- Andrews T, Gregory JM, Webb MJ, Taylor KE (2012). Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models. Geophys Res Lett 39(9). doi: 10.1029/2012GL051607
- Chung ES, Soden BJ, Sohn BJ (2010). Revisiting the determination of climate sensitivity from relationships between surface temperature and radiative fluxes. Geophys Res Lett 37(10). doi: 10.1029/2010GL043051
- Geoffroy O, Saint-Martin D, Bellon G, Voldoire A, Olivié DJL, Tytéca S (2013) Transient climate response in a two-layer energy-balance model. Part II: representation of the efficacy of deep-ocean heat uptake and validation for CMIP5 AOGCMs. J Clim 26(6):1859–1876. doi: 10.1175/JCLI-D-12-00196.1 CrossRefGoogle Scholar
- Gregory JM, Forster PM (2008). Transient climate response estimated from radiative forcing and observed temperature change. J Geophys Res-Atmos 113(D23). doi: 10.1029/2008JD010405
- Intergovernmental Panel on Climate Change (IPCC) (2013) Climate change 2013: the physical science basis. In: Stocker TF et al (eds) Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, CambridgeGoogle Scholar
- Lindzen RS, Choi YS (2009). On the determination of climate feedbacks from ERBE data. Geophys Res Lett 36(16). doi: 10.1029/2009GL039628
- Murphy DM (2010). Constraining climate sensitivity with linear fits to outgoing radiation. Geophys Res Lett 37(9). doi: 10.1029/2010GL042911
- Spencer RW, Braswell WD (2010). On the diagnosis of radiative feedback in the presence of unknown radiative forcing. J Geophys Res-Atmos 115(D16)Google Scholar
- Trenberth KE, Fasullo JT, O’Dell C, Wong T (2010). Relationships between tropical sea surface temperature and top-of-atmosphere radiation. Geophys Res Lett 37(3). doi: 10.1029/2009GL042314