What aspect of model performance is the most relevant to skillful future projection on a regional scale?

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dc.contributor.authorLi, Tong
dc.contributor.authorZhang, Xuebin
dc.contributor.authorJiang, Zhihong
dc.date.accessioned2025-04-10T20:27:43Z
dc.date.available2025-04-10T20:27:43Z
dc.date.issued2024
dc.description.abstractWeighting models according to their performance has been used to produce multimodel climate change projections. But the added value of model weighting for future projection is not always examined. Here we apply an imperfect model framework to evaluate the added value of model weighting in projecting summer temperature changes over China. Members of large-ensemble simulations by three climate models of different climate sensitivities are used as pseudo-observations for the past and the future. Performance of the models participating in the phase 6 of the Coupled Model Intercomparison Project (CMIP6) are evaluated against the pseudo-observations based on simulated historical climatology and trends in global, regional, and local temperatures to determine the model weights for future projection. The weighted projections are then compared with the pseudo-observations in the future period. We find that regional trend as a metric of model performance yields generally better skill for future projection, while past climatology as performance metric does not lead to a significant improvement to projection. Trend at the grid-box scale is also not a good performance indicator as small-scale trend is highly uncertain. For the model weighting to be effective, the metric for evaluating the model’s performance must be relatable to future changes, with the response signal separable from internal variability. Projected summer warming based on model weighting is similar to that of unweighted projection but the 5th–95th-percentile uncertainty range of the weighted projection is 38% smaller with the reduction mainly in the upper bound, with the largest reduction appearing in southeast China.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.description.sponsorshipWe acknowledge Lukas Brunner and Ruth Lorenz for publishing their weighting code. This research was supported by the National Natural Science Foundation of China (Grant 42275184) and the National Key Research and Development Program of China (Grant 2017YFA0603804), and the Postgraduate Research and Practice Innovation Program of Government of Jiangsu Province (KYCX21_0940).
dc.identifier.citationLi, T., Zhang, X., & Jiang, Z. (2024). What aspect of model performance is the most relevant to skillful future projection on a regional scale? Journal of Climate, 37(5), 1567–1580. https://doi.org/10.1175/JCLI-D-23-0312.1
dc.identifier.urihttps://doi.org/10.1175/JCLI-D-23-0312.1
dc.identifier.urihttps://hdl.handle.net/1828/21864
dc.language.isoen
dc.publisherJournal of Climate
dc.subjectclimate change
dc.subjectclimate models
dc.subjectensembles
dc.subjecttrends
dc.subjectUN SDG 13: Climate Action
dc.subjectuncertainty
dc.subject#journal article
dc.subjectPacific Climate Impacts Consortium (PCIC)
dc.titleWhat aspect of model performance is the most relevant to skillful future projection on a regional scale?
dc.typeArticle

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