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What aspect of model performance is the most relevant to skillful future projection on regional scale?
  • Tong Li,
  • Xuebin Zhang,
  • Zhihong Jiang
Tong Li
Nanjing University of Information Science and Technology
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Xuebin Zhang
Climate Research Division, Environment Canada
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Zhihong Jiang
Nanjing University of Information Science and Technology

Corresponding Author:zhjiang@nuist.edu.cn

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Weighting models according to their performance has been used in constructing multi-model regional climate change scenarios. But the added value of model weighting is not always examined. Here we apply an imperfect model framework to examine the added value of model weighting in projecting summer temperature changes over China. Members of large ensemble initial condition simulations by three climate models of different climate sensitivities under the historical forcing and future scenarios are used as pseudo-observations. Performance of the models participating in the 6th phase of the coupled model intercomparison project (CMIP6) in simulating past climate are evaluated against the pseudo-observations based on climatology, trends in global, regional and local temperatures. The performance along with model’s independence are used to determine the model weights for future projection. The weighted projections are then compared with the pseudo-observations for the future. We find that regional trend as a metric of model performance yields the best skill for future projection while past climatology as performance metric does not improve future projection. Trend at the grid-box scale is also not a good performance indicator as small scale trend is highly uncertain. Projected summer warming based on model weighting is similar to that of unweighted projection, at 2.3°C increase relative to 1995-2014 by the middle of the 21st century under SSP8.5 scenario, but the 5th-95th uncertainty range of the weighted projection is 18% smaller with the reduction mainly in the upper bound, with the largest reduction in the northern Tibetan Plateau.