An Assessment of GCM Performance at a Regional Scale Using a Score-Based Method

Hindawi Advances in Meteorology Volume 2018, Article ID 7641019, 12 pages https://doi.org/10.1155/2018/7641019 Research Article An Assessment of GCM Performance at a Regional Scale Using a Score-Based Method Fangxin Shi ,1 Zhihui Wang ,1 Liang Qi,2 and Rongxu Chen1 1 2 Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China The Office of Yellow River Flood Control and Drought Relief Headquarters, Zhengzhou 450003, China Correspondence should be addressed to Zhihui Wang; Received 19 April 2018; Revised 5 August 2018; Accepted 19 September 2018; Published 8 November 2018 Academic Editor: Pedro Salvador Copyright © 2018 Fangxin Shi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A multicriteria score-based method was developed to assess the performances of 18 general circulation models (GCMs) in the study region from 1970 to 2005. The results indicate the following. (1) GCMs simulate temperature better than rainfall. The temporal and spatial distributions of simulated temperature performed well compared with those from the observations. In comparison to temperature, the spatial distribution of simulated precipitation performed poorly. Most of the GCMs underestimated temperature and overestimated precipitation. (2) The Grubbs test was used to detect anomalous moving changes in the rank score (RS) results; the inm-cm4 and ipsl-cm5b-lr models were rejected when simulating temperature, while the bnu-esm and canesm2 models performed poorly when simulating precipitation. (3) Adding or removing any criterion does not significantly influence the RS result, which indicates that the multicriteria score-based method is robust. The advantages of using multicriteria score-based method to assess GCMs performance were demonstrated, and this method also provides a more comprehensive assessment when compared with the single-criterion method. The multicriteria method could replace other criteria as the research requirements and could be easily extended to different study regions; the results could be used for better informed regional climate change impact analyses. 1. Introduction General circulation models (GCMs) are the most common tools for projecting future climate change. Errors and uncertainties in GCM metadata range in severity, specifically resulting in the inability to simulate observed meteorological events. GCM simulations are often characterized by biases and uncertainties that limit their direct application [1]. Different forcing scenarios, GCMs, and subgrid-scale forcings and processes cause uncertainties, revealing an abundance of information but also indicate that a large amount of work is required to identify useful information, which limits GCM applications [2]. Despite continuous efforts to improve the GCM simulation performance, the application of assessment methods is essential for climate change impact studies [3]. To improve the accuracy of GCM applications, GCMs have been assessed in many studies [4–6]. These assessments emphasize various aspects of GCMs according to their different applications. For example, in one study, where a long-term climate change analysis was the main focus, an assessment of the GCM performance before its application only focused on its long-term temporal and spatial distribution simulations. However, the drawback of this assessment was that using a single criterion could only describe the temporal or spatial performances of the GCMs but may not meet the other requirements of the study [4]. A more comprehensive understanding of the advantages and disadvantages of GCMs is possible when more criteria are included in a GCM assessment. To date, no assessment method used in the study of GCMs has been widely accepted. Assessing the performance of GCMs before using them is becoming an interesting issue. In this paper, a multicriteria score-based method was analyzed and the performances of all GCMs were quantitatively calculated and examined. We studied this method with the aim of comprehensively and accurately evaluating the performances of the GCMs. The outline of the paper is as follows: the data and methods are presented in Sections 2 and 3, respectively. Section 4 describes the performance of each GCM. The GCM 2 simulations of temperature and precipitation are evaluated in the study region. The concluding remarks are provided in Section 5. 2. Study Region and Dataset The performances of the GCMs in the Yellow-Huai-Hai region were assessed in this study. The Yellow-Huai-Hai region, which is located in north-central China between 30° and 42.5°N and 90° and 122.5°E (Figure 1), has the largest fluvial plain in China. Most parts of the study area are semiarid and semihumid (i.e., the Yellow River and Hai River basins, respectively), and only a small part of the region in the southeast of the study area has a humid climate (the area covering the Huai River basin). The Yellow-Huai-Hai region is an agricultural breadbasket and prime urban and industrial region in China. This region, therefore, plays an important role in the social and economic development of the country. Thus, the consequences of climate change seriously restrain economic growth [7, 8]. All GCM data are from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), which is the most important tool for analyzing future climate change. The dataset from this project provides a framework for coordinating climate change experiments with the aim of evaluating climate simulations of the recent past to provide more accurate projections of climate change and quantifications of climate feedbacks compared to those from CMIP3 [9]. Details on the data can be found in Moss et al. [10] and Taylor et al. [9]. To simulate future climate change, 18 global GCMs from CMIP5 were considered in this paper (including access1-0, bcc-csm1, bnu-esm, canesm2, ccsm4, cesm1-bgc, cnrm-cm5, giss-e2-h, csiro-mk3.6, fgoals-g2, gfdl-cm3, hadgem2, inm-cm4, noresm1-m, miroc-esm, ipsl-cm5b-lr, mri-cgcm3). More details on all of the models are available at http://cmip-pcmdi.llnl. gov/cmip5/docs/CMIP5_modeling_groups.docx. Since GCM horizontal resolutions vary, the GCM outputs were interpolated to a uniform resolution of 2.5° × 2.5°. The grid cell distributions over the study region are shown in Figure 1. High-quality temperature and precipitation data were derived from the daily dataset on China’s surface climate (V3.0) during the period 1970–2005 provided by the National Meteorological Information Center. These data are based on gauged data from 128 meteorological stations (Figure 1) and have been controlled for quality and accuracy by nearly 100%; for more details, see http://data.cma. cn/data/cdcdetail/dataCode/SURF_CLI_CHN_MUL_DAY_ V3.0.html. To effectively assess the performances of the GCMs, the daily data observed by the (...truncated)