Super-ensemble of three RCMs for climate projection over East Asia and Taiwan

Liu, Chung Ming; Wu, Ming Chin; Paul, Sahana; Chen, Ying Chen; Lin, Sheng Huang; Lin, Wei Shiang; Lee, Yen Chih; Hsu, Huang‐Hsiung; Tseng, Ren Yow; Chen, Cheng Ta

doi:10.1007/s00704-010-0275-x

Cited by 10 publications

(9 citation statements)

References 48 publications

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“…Several different means of weighting different regional climate models in an ensemble have been suggested by Liu et al (2010). They compare three weighting methods.…”

Section: Assigning Different Weights To Each Model In a Multi-model Ementioning

confidence: 99%

Lessons from climate modeling on the design and use of ensembles for crop modeling

et al. 2016

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Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically Climatic Change (2016) 139:551-564 DOI 10.1007/s10584-016-1803-1 Highlights ?•Ensembles of crop models can improve uncertainty estimates, impact projections, and collaboration.•The climate modeling community has identified and studied many of the questions related to design and analysis of ensembles.•Much of the climate experience could be adapted to crop modeling. oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

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“…Several different means of weighting different regional climate models in an ensemble have been suggested by Liu et al (2010). They compare three weighting methods.…”

Section: Assigning Different Weights To Each Model In a Multi-model Ementioning

confidence: 99%

Lessons from climate modeling on the design and use of ensembles for crop modeling

et al. 2016

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“…Previous modeling studies have found that the horizontal resolution of GCMs is generally too coarse to accurately simulate the rainfall distribution over local areas with complex terrain (such as Taiwan) and that the dynamical downscaling of GCMs by regional climate models can help reduce the bias in rainfall simulations over complex areas [e.g., Huang et al ., ; Hong and Kanamitsu , ; Lee and Hong , ; Lee et al ., ; Xue et al ., ]. Because a reasonable present‐day simulation is crucial for reliable future projections, most studies that have assessed the effects of future climate change in Taiwan have adopted the dynamical downscaling approach [e.g., Liang et al ., ; Hsu et al ., ; Liu et al ., ; Lin et al ., ; Huang et al ., ]. Among these dynamical downscaling studies, many have focused on the total summer rainfall [e.g., Liu et al ., ; Hsu et al ., ] or a specific type of rainfall [e.g., Huang et al ., ].…”

Section: Introductionmentioning

confidence: 99%

“…Since the projections of future climate change made by global climate models (GCMs) are widely accessible, increasing attention has been given to how global and local rainfall will likely to be altered in the future [e.g., Liang et al ., ; Hsu et al ., ; Liu et al ., ]. Previous modeling studies have found that the horizontal resolution of GCMs is generally too coarse to accurately simulate the rainfall distribution over local areas with complex terrain (such as Taiwan) and that the dynamical downscaling of GCMs by regional climate models can help reduce the bias in rainfall simulations over complex areas [e.g., Huang et al ., ; Hong and Kanamitsu , ; Lee and Hong , ; Lee et al ., ; Xue et al ., ].…”

Section: Introductionmentioning

confidence: 99%

Dynamical downscaling simulation and future projection of summer rainfall in Taiwan: Contributions from different types of rain events

et al. 2016

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Summer rainfall in Taiwan is composed of four types of rain events: tropical cyclone (TC), frontal convection (FC), diurnal convection (DC), and other southerly convection (SC) that propagates from the nearby ocean. In this study, we accessed the present‐day simulation (1979–2003) and future projection (2075–2099, the Representative Concentration Pathway 8.5 scenario) of rainfall in Taiwan by using the regional Weather Research and Forecasting model driven by the global High Resolution Atmospheric Model. The results indicated that the dynamical downscaling process adds value to the present‐day simulation of summer rainfall in Taiwan and the contribution of different types of rain events. It was found that summer rainfall in Taiwan will increase in a warmer future and that this change was mainly due to an increase in SC rainfall (i.e., light rainfall event). The following trends in Taiwan rainfall were also predicted in a warmer future: (1) SC rainy days will increase because the intensified monsoonal flow facilitates the propagation of more SC toward Taiwan, (2) TC rainy days will decrease as the Western North Pacific subtropical high extends southwestward and prevents TC systems from passing over Taiwan, (3) DC rainy days will decrease in response to the increased local thermal stability, and (4) FC rainy days will show no significant changes. Moreover, all types of rainfall are projected to become more intense in the future due to the increased moisture supply in a warmer climate. These findings highlight how the rainfall characteristics in East Asia may change in response to climate change.

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“…The most common solution to reduce such model uncertainties is to combine results from a range of models, the so-called multi-model ensemble approach (e.g. , Déqué et al 2007, Jacob et al 2007, Tebaldi & Knutti 2007, Van der Linden & Mitchell 2009, Liu et al 2011, Iizumi et al 2012. Following this approach, the Coordinated Regional Climate Downscaling Experiment − East Asia (CORDEX-EAS: http://cordex-ea.climate.go.kr/), which aimed at improving coordination of international efforts, was initiated for the East Asia region.…”

Section: Introductionmentioning

confidence: 99%

Climate projections for Vietnam based on regional climate models

Ngo‐Duc¹,

Kieu²,

Thatcher³

et al. 2014

Clim. Res.

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This study uses an ensemble of regional climate models (RCMs) to simulate and project the climate of Vietnam. Outputs of 3 global climate models are dynamically downscaled using 3 RCMs. Experiments are performed for a baseline period from 1980 to 1999 and for a future projection from 2000 to 2050 with the A1B emission scenario. Verifications against observations at 61 selected meteorological stations in the region show that an ensemble mean of the 3 RCMs outperforms the individual RCM in representing the climatological mean state, and reasonably captures some extreme climate indices such as the annual maximum daily temperature (TXx), the annual minimum daily temperature (TNn), and the annual maximum 1 d precipitation (RX1day). Future ensemble projections of the temperature, precipitation, and 3 different extreme indices are then evaluated. The simulations predict the 2 m air temperature over Vietnam to significantly increase in both the near future 2011−2030 and middle-future 2031−2050 periods compared to the baseline period. The temperature trend tends to be positive and significant over the whole Vietnam for the spring, summer and fall periods, whereas it is insignificant in the north central region during winter. The highest increase of ~0.5°C decade −1 appears to be pronounced in summer. For precipitation, future changes vary depending on regions and seasons, with the most significant increasing trend over the coastal plain of Central Vietnam, particularly during the winter monsoon season. Under the global warming scenario A1B, TXx and TNn show a significant increase, with the highest rate in the northern and central highlands regions of Vietnam. The extreme precipitation RX1day indices show increasing trends for the coastal zone in the south central region of Vietnam, suggesting more severe water-related disasters in this region in the future. KEY WORDS: Dynamical downscaling · Multi-model approach · Climate extreme indices · VietnamResale or republication not permitted without written consent of the publisher

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Super-ensemble of three RCMs for climate projection over East Asia and Taiwan

Cited by 10 publications

References 48 publications

Lessons from climate modeling on the design and use of ensembles for crop modeling

Lessons from climate modeling on the design and use of ensembles for crop modeling

Dynamical downscaling simulation and future projection of summer rainfall in Taiwan: Contributions from different types of rain events

Climate projections for Vietnam based on regional climate models

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