Evaluation of multiple regional climate models for summer climate extremes over East Asia

Park, Changyong; Min, Seung‐Ki; Lee, Dong-Hyun; Cha, Dong‐Hyun; Suh, Myoung‐Seok; Kang, Hyun‐Suk; Hong, Song‐You; Lee, Dong‐Kyou; Baek, Hee-Jeong; Boo, Kyung‐On; Kwon, Won‐Tae

doi:10.1007/s00382-015-2713-z

Cited by 146 publications

(125 citation statements)

References 39 publications

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“…go.kr/cordex/download.do, last access: 18 May 2018). A series of studies based on RCMs within CORDEX-EA has been conducted to project extreme and mean precipitation and temperature in East Asia (Jin et al, 2016;Lee et al, 2014;Niu et al, 2015;Park et al, 2016;Tang et al, 2016;Um et al, 2017), but little attention has been paid to quantify the contributions of the uncertainty in future climate projection over China.…”

Section: H Gu Et Al: Ensemble Future Climate Projections and Uncertmentioning

confidence: 99%

“…Model configurations including physical schemes are summarized in Table 1. Please refer to the references Suh et al (2012) and Park et al (2016) for more details on RCMs used in this study.…”

Section: Models and Experimentsmentioning

confidence: 99%

“…HadGEM2-AO (1.875 • × 1.25 • horizontal resolution) has been used for climate simulations in a CMIP5 set of longterm experiments and has been demonstrated to have a reasonable ability to capture the East Asian climatology (Baek Table 1. RCMs used in this study * (Park et al, 2016 The multi-model ensemble (MME) mean, defined as the pointwise arithmetic average over all individual model climatologies, narrows down inter-RCM uncertainties because of their differences in model structures and physics. To further evaluate the model performance on smaller spatial scales, we evaluate the performance of RCMs over five selected subregions (as shown in Fig.…”

Section: Models and Experimentsmentioning

confidence: 99%

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High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

Yang

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. An ensemble simulation of five regional climate models (RCMs) from the coordinated regional downscaling experiment in East Asia is evaluated and used to project future regional climate change in China. The influences of model uncertainty and internal variability on projections are also identified. The RCMs simulate the historical climate and future climate projections under the Representative Concentration Pathway (RCP) RCP4.5 scenario. The simulations for five subregions in China, including northeastern China, northern China, southern China, northwestern China, and the Tibetan Plateau, are highlighted in this study. Results show that (1) RCMs can capture the climatology, annual cycle, and interannual variability of temperature and precipitation and that a multimodel ensemble (MME) outperforms that of an individual RCM. The added values for RCMs are confirmed by comparing the performance of RCMs and global climate models (GCMs) in reproducing annual and seasonal mean precipitation and temperature during the historical period. (2) For future (2030)(2031)(2032)(2033)(2034)(2035)(2036)(2037)(2038)(2039)(2040)(2041)(2042)(2043)(2044)(2045)(2046)(2047)(2048)(2049) climate, the MME indicates consistent warming trends at around 1 • C in the entire domain and projects pronounced warming in northern and western China. The annual precipitation is likely to increase in most of the simulation region, except for the Tibetan Plateau. (3) Generally, the future projected change in annual and seasonal mean temperature by RCMs is nearly consistent with the results from the driving GCM. However, changes in annual and seasonal mean precipitation exhibit significant inter-RCM differences and possess a larger magnitude and variability than the driving GCM. Even opposite signals for projected changes in average precipitation between the MME and the driving GCM are shown over southern China, northeastern China, and the Tibetan Plateau. (4) The uncertainty in projected mean temperature mainly arises from the internal variability over northern and southern China and the model uncertainty over the other three subregions. For the projected mean precipitation, the dominant uncertainty source is the internal variability over most regions, except for the Tibetan Plateau, where the model uncertainty reaches up to 60 %. Moreover, the model uncertainty increases with prediction lead time across all subregions.

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Section: H Gu Et Al: Ensemble Future Climate Projections and Uncertmentioning

confidence: 99%

Section: Models and Experimentsmentioning

confidence: 99%

Section: Models and Experimentsmentioning

confidence: 99%

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High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

Yang

et al. 2018

Hydrol. Earth Syst. Sci.

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“…Most approaches include evaluating models' ability to represent past climate conditions (e.g. Maraun et al 2015;Park et al 2016) as a measure of reliability for future climate simulations. Model weighting (e.g.…”

Section: Existing Approaches To Combining Different Climate Model Simmentioning

confidence: 99%

Providing future climate projections using multiple models and methods: insights from the Philippines

et al. 2018

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To meet the growing demand for climate change information to guide national and local adaptation decision-making in the Philippines, the climate science and services community is producing an increasing volume of future climate data using a range of modelling approaches. However, there is a significant methodological challenge in how to best compare and combine information produced using different models and methods. In this paper, we present the landscape of climate model data available in the Philippines and show how multimodel, multi-method climate projections are being used and communicated to inform climate Climatic Change (2018) change policy and planning, focusing on the agriculture sector. We highlight the importance of examining and communicating methodological strengths and weaknesses as well as understanding the needs and capabilities of different user communities. We discuss the assessment of projections from different methods, including global and regional downscaled simulations, and discuss ways to summarise and communicate this information to stakeholders using coproduction approaches. The paper concludes with perspectives on how to best use an Bensemble of opportunity^to construct defensible, plausible and usable climate projections.

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“…The spatial resolution of the data is 50 km (except HadGEM3-RA is 0.44°), and the whole CORDEX-EA 30 domain includes East Asia, India, the Western Pacific Ocean, and the northern part of Australia, as shown in Fig. 1 Suh et al (2012) and Park et al (2016). Table 1 Figure 1…”

Section: Models and Experimentsmentioning

confidence: 99%

High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

Gu¹,

Yu²,

Yang³

et al. 2017

Preprint

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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)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) 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 20 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.

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Evaluation of multiple regional climate models for summer climate extremes over East Asia

Cited by 146 publications

References 39 publications

High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

Providing future climate projections using multiple models and methods: insights from the Philippines

High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

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