Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-407
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
11 Sep 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).
High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia
Huanghe Gu1,2, Zhongbo Yu1,2, Chuanguo Yang1,2, Qin Ju1,2, and Tao Yang1,2 1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China
2College of Hydrology and Water Resources, Hohai University, Nanjing, China
Abstract. An ensemble simulation of 5 regional climate models (RCMs) from the Coordinated Regional Downscaling Experiment in East Asia (CORDEX-East Asia) was evaluated and used for future regional climate change projection in China. Meanwhile, the contributions of model uncertainty and internal variability are identified. The RCMs simulated both the historical climate (1989–2008) and future climate projection (2030–2049) under the Representative Concentration Pathway (RCP) RCP4.5 scenario. We highlighted 5 subregions in China, viz. Northeast China, North China, South China, Northwest China, and Tibetan Plateau. Our results showed that the capability of RCMs to capture the climatology, annual cycle and inter-annual variability of temperature and precipitation and multi-model ensemble outperforms the individual RCM. For the future climate, consistent warming trends around 1 °C were indicated by multi-model ensemble over the whole domain and more pronounced warming was projected in northern and western China. The annual precipitation is likely to increase in most of the simulation region, except the Tibetan Plateau which decreases −7.8 %. Compare with the similar seasonal temperature changes with the driving global climate model (GCM), the seasonal precipitation change shows significant inter-RCM difference and has larger magnitude and variability than driving GCM. The model uncertainty for future temperature projection is clearly dominant over the northeast, northwest China and Tibetan Plateau, reaching up to 70 %, and it contribute about 40 % of the total uncertainty over north and south China. For precipitation, the internal variability is dominant over most regions except for the Tibetan Plateau which the model uncertainties reach up to 60 %. In addition, the model uncertainty increases with prediction lead time over all subregions.

Citation: Gu, H., Yu, Z., Yang, C., Ju, Q., and Yang, T.: High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-407, in review, 2017.
Huanghe Gu et al.
Huanghe Gu et al.
Huanghe Gu et al.

Viewed

Total article views: 159 (including HTML, PDF, and XML)

HTML PDF XML Total BibTeX EndNote
130 26 3 159 0 1

Views and downloads (calculated since 11 Sep 2017)

Cumulative views and downloads (calculated since 11 Sep 2017)

Viewed (geographical distribution)

Total article views: 159 (including HTML, PDF, and XML)

Thereof 155 with geography defined and 4 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 19 Sep 2017
Publications Copernicus
Download
Short summary
An ensemble simulation of 5 RCMs from the CORDEX in East Asia was evaluated and used for future regional climate change projection in China. Meanwhile, the contributions of model uncertainty and internal variability are identified. We found that the multi-model ensemble outperforms the individual RCM in historical climate simulation. The future climate projections show significant inter-RCM difference and the model uncertainty increases with prediction lead time over all subregions.
An ensemble simulation of 5 RCMs from the CORDEX in East Asia was evaluated and used for future...
Share