Considerable variability exists in simulations of the future climate. This variability is caused by differences in the parameterisations across general circulation models (GCMs), the initial conditions used and the different assumptions made as to how emissions will evolve in the future. As a result, there is considerable disagreement between available projections of climate variables, which can be used to quantify the uncertainty each variable exhibits. This leads to the question—which variables (or set of variables) are more reliable for use in climate change impact assessments. This research presents a framework to quantify the relative reliability amongst a range of upper air atmospheric variables. This is made possible by pooling simulations across multiple models, trajectories (scenarios) and initial conditions in a rank-transformed space. A metric named the variable reliability score (VRS) assesses the relative reliabilities across different atmospheric variables on a common scale. The VRS has been applied to calculate the total reliability as well as reliability from each source of uncertainty, namely model, scenarios and initial conditions. This comparison helps to decide if more models, scenarios or ensembles are required for uncertainty analysis of climate change impact assessment.
The variables compared include geopotential height and its north-south difference, specific humidity, eastward wind and northward wind, all at the 500 and 850 hPa pressure levels. These variables were chosen based on availability of data and their documented use in previous climate change impact assessment studies worldwide. A regional assessment of VRS over 21 regions around the world shows that though the magnitude of VRS varies spatially, the ranked reliability of the variable rank remains relatively similar. On average, the lowest reliability is associated with geopotential height, whilst wind speeds and the north-south difference of geopotential height have higher reliability.
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The Australian Research Council is acknowledged for financial support for this study. The World Climate Research Programme’s Working Group on Coupled Modelling, responsible for CMIP, is acknowledged for making model simulations in Table 1 available. NOAA/OAR/ESRL PSD is acknowledged for the NCEP reanalysis data used in this study.
Hawkins E, Sutton R (2011) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn 37:407–418CrossRefGoogle Scholar
Huth R (1999) Statistical downscaling in central Europe: evaluation of methods and potential predictors. Clim Res 13:91–101CrossRefGoogle Scholar
IPCC (2013) Climate Change 2013: The Physical Science Basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
Knutti R, Sedláček J, Sanderson BM, Lorenz R, Fischer EM, Eyring V (2017) A climate model projection weighting scheme accounting for performance and interdependence. Geophys Res Lett. https://doi.org/10.1002/2016gl072012
Koster RD et al (2006) GLACE: the global land-atmosphere coupling experiment. Part I: Overview. J Hydromet 7:590–610CrossRefGoogle Scholar
Mehrotra R, Sharma A (2006) A nonparametric stochastic downscaling framework for daily rainfall at multiple locations. J Geophys Res 111:D15101. https://doi.org/10.1029/2005JD006637
Pui A, Sharma A, Santoso A, Westra S (2012) Impact of the El Nino-southern oscillation, Indian Ocean dipole, and southern annular mode on daily to subdaily rainfall characteristics in East Australia. Mon Weather Rev 140:1665–1682CrossRefGoogle Scholar
Sachindra DA, Huang F, Barton A, Perera BJC (2014a) Statistical downscaling of general circulation model outputs to precipitation-part 1: calibration and validation. Int J Climatol: n/a-n/a https://doi.org/10.1002/joc.3914
Sachindra DA, Huang F, Barton A, Perera BJC (2014b) Statistical downscaling of general circulation model outputs to precipitation—part 1: calibration and validation. Int J Climatol 34:3264–3281. https://doi.org/10.1002/joc.3914
Van Vuuren DP et al (2011) The representative concentration pathways: an overview. Clim Chang 109:5–31CrossRefGoogle Scholar
Woldemeskel FM, Sharma A, Sivakumar B, Mehrotra R (2012) An error estimation method for precipitation and temperature projections for future climates J Geophys Res Atmos 117:D22104. https://doi.org/10.1029/2012JD018062
Woldemeskel F, Sharma A, Sivakumar B, Mehrotra R (2016) Quantification of precipitation and temperature uncertainties simulated by CMIP3 and CMIP5 models. J Geophys Res: Atmos 121:3–17CrossRefGoogle Scholar