2,022
Views
11
CrossRef citations to date
0
Altmetric
Research Article

On the relationship between climate sensitivity and modelling uncertainty

, &
Article: 1327765 | Received 02 Feb 2017, Accepted 26 Apr 2017, Published online: 09 Jun 2017

References

  • Cohen, A. E., Cavallo, S. M., Coniglio, M. C. and Brooks, H. E. 2015. A review of planetary boundary layer parameterization schemes and their sensitivity in simulating southeastern U.S. cold season severe weather environments. Wea. Forecast. 30, 591–612.10.1175/WAF-D-14-00105.1
  • Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C. and co-authors. 2013. Evaluation of climate models. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Vol. 5 (eds. T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 741–866.
  • Giorgi, F. and Mearns, L. O. 2002. Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the ‘reliability ensemble averaging’(REA) method. J. Clim. 15, 1141–1158.10.1175/1520-0442(2002)015<1141:COAURA>2.0.CO;2
  • Grose, M. R., Brown, J. N., Narsey, S., Brown, J. R., Murphy, B. F. and co-authors 2014. Assessment of the CMIP5 global climate model simulations of the western tropical Pacific climate system and comparison to CMIP3. Int. J. Climatol. 34, 3382–3399. DOI: 10.1002/joc.3916.
  • Hawkins, E. and Sutton, R. 2009. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soci. 90, 1095–1107.10.1175/2009BAMS2607.1
  • Hawkins, E. and Sutton, R. 2011. The potential to narrow uncertainty in projections of regional precipitation change. Clim. Dyn. 37, 407–418.10.1007/s00382-010-0810-6
  • IPCC. 2014. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge University Press, Cambridge, UK.
  • Katzfey, J., Nguyen, K., McGregor, J., Hoffmann, P., Ramasamy, S. and co-authors. 2016. High-resolution simulations for vietnam – methodology and evaluation of current climate. J. Atmos. Sci. 52(2), 1–16.
  • Knutti, R., Allen, M. R., Friedlingstein, P., Gregory, J. M., Hegerl, G. C. and co-authors 2008. A review of uncertainties in global temperature projections over the twenty-first century. J. Clim. 21, 2651–2663.10.1175/2007JCLI2119.1
  • Knutti, R. and Sedláček, J. 2013. Robustness and uncertainties in the new CMIP5 climate model projections. Nat. Clim. Change 3, 369–373.
  • Mearns, L. O., Arritt, R., Biner, S., Bukovsky, M. S., McGinnis, S. and co-authors. 2012. The North American regional climate change assessment program: overview of phase I results. Bull. Am. Meteorol. Soc. 93, 1337–1362.10.1175/BAMS-D-11-00223.1
  • Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K. and co-authors. 2010. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756.10.1038/nature08823
  • Murphy, J. M., Sexton, D. M., Barnett, D. N., Jones, G. S., Webb, M. J. and co-authors 2004. Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430, 768–772.10.1038/nature02771
  • Northrop, P. J. and Chandler, R. E. 2014. Quantifying sources of uncertainty in projections of future climate*. J. Clim. 27, 8793–8808.10.1175/JCLI-D-14-00265.1
  • Oppenheimer, M., Campos, M., Warren, R., Birkmann, J., Luber, G. and co-authors. 2014. Emergent risks and key vulnerabilities. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds. C. B. Field, V. R. Barros, D. J. Dokken, K. J. Mach, M. D. Mastrandrea, T. E. Bilir, M. Chatterjee, K. L. Ebi, Y. O. Estrada, R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy, S. MacCracken, P. R. Mastrandrea and L. L. White). Cambridge University Press, Cambridge, UK, pp. 1039–1099.
  • Sherwood, S. C., Bony, S. and Dufresne, J.-L. 2014. Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505, 37–42.10.1038/nature12829
  • Stanfield, R. E., Jiang, J. H., Dong, X., Xi, B., Su, H. co-authors. 2016. A quantitative assessment of precipitation associated with the ITCZ in the CMIP5 GCM simulations. Clim. Dyn. 47, 1863–1880.
  • Stephenson, D. B., Collins, M., Rougier, J. C. and Chandler, R. E. 2012. Statistical problems in the probabilistic prediction of climate change. Environmetrics 23, 364–372.10.1002/env.2153
  • Taylor, K. E., Stouffer, R. J. and Meehl, G. A. 2012. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485.10.1175/BAMS-D-11-00094.1
  • Tebaldi, C., Smith, R. L., Nychka, D. and Mearns, L. O. 2005. Quantifying uncertainty in projections of regional climate change: a bayesian approach to the analysis of multimodel ensembles. J. Clim. 18, 1524–1540.10.1175/JCLI3363.1
  • Watterson, I. G., Bathols, J. and Heady, C. 2013a. What influences the skill of climate models over the continents? Bull. Amer. Meteor. Soc. 95(5), 689–700. DOI: 10.1175/BAMS-D-12-00136.1
  • Watterson, I. G., Hirst, A. C. and Rotstayn, L. D. 2013b. A skill-score based evaluation of simulated Australian climate. Aust. Meteor. Oceanogr. J. 63, 181–190.10.22499/2.00000
  • Yip, S., Ferro, C. A., Stephenson, D. B. and Hawkins, E. 2011. A simple, coherent framework for partitioning uncertainty in climate predictions. J. Clim. 24, 4634–4643.10.1175/2011JCLI4085.1
  • Ylhäisi, J. S., Garrè, L., Daron, J. and Räisänen, J. 2015. Quantifying sources of climate uncertainty to inform risk analysis for climate change decision-making. Local Environ. 20, 811–835.10.1080/13549839.2013.874987