Abstract
A weighting scheme jointly considering model performance and independence (PI-based weighting scheme) is employed to deal with multi-model ensemble prediction of precipitation over China from 17 global climate models. Four precipitation metrics on mean and extremes are used to evaluate the model performance and independence. The PI-based scheme is also compared with a rank-based weighting scheme and the simple arithmetic mean (AM) scheme. It is shown that the PI-based scheme achieves notable improvements in western China, with biases decreasing for all parameters. However, improvements are small and almost insignificant in eastern China. After calibration and validation, the scheme is used for future precipitation projection under the 1.5 and 2°C global warming targets (above preindustrial level). There is a general tendency to wetness for most regions in China, especially in terms of extreme precipitation. The PI scheme shows larger inhomogeneity in spatial distribution. For the total precipitation PRCPTOT (95th percentile extreme precipitation R95P), the land fraction for a change larger than 10% (20%) is 22.8% (53.4%) in PI, while 13.3% (36.8%) in AM, under 2°C global warming. Most noticeable increase exists in central and east parts of western China.
Highlights
Climate change under global warming is a major challenge for many natural ecosystems on the earth and for human societies (IPCC, 2013; WMO, 2019)
3.2 Rank-based weighting scheme In order to assess the proposed weighting scheme, we evaluate it against two largely-used algorithms, the Rank-based weighting scheme (Chen et al, 2011; Jiang et al, 2015; Li et al, 2016) and arithmetic mean, the latter serving as a baseline
Σd in western China is larger than its counterpart in eastern China, models closer to observation get higher weights, which indicates that the inconsistence among models in the west is more significant
Summary
Climate change under global warming is a major challenge for many natural ecosystems on the earth and for human societies (IPCC, 2013; WMO, 2019). (Xu et al, 2013; Aslam et al, 2017; Guirguis et al, 2018) These extreme climate events provoke substantial economic losses and civilian casualties, which raises the urgency of searching adaptation and mitigation measures to combat climate change (Jones et al, 2014; Li et al, 2018; Zhan et al, 2018; Chen et al, 2020). It assumes that the range of models’ projections is representative of what we believe is the uncertainty. These are all strong assumptions, not always satisfied. The reality is that some models are worse than others in how well they represent the observed mean climate and trend (Eyring et al, 2015; Baumberger et al, 2017). The common practice is using the ability of model’s simulated patterns against observations as a measure of model’s skill (Perkins et al, 2009; Qi et al, 2017)
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