Performance of the Taiwan Earth System Model in Simulating Climate Variability Compared With Observations and CMIP6 Model Simulations

Wang, Yi‐Chi; Hsu, Huang‐Hsiung; Chen, Chao‐An; Tseng, Wan‐Ling; Hsu, Pei-Chun; Lin, Cheng‐Wei; Chen, Yu-Luen; Jiang, Li-Chiang; Lee, Yu‐Chi; Liang, Hsin-Chien; Chang, Wen-Ming; Lee, Wei‐Liang; Shiu, Chengshi

doi:10.1029/2020ms002353

Cited by 48 publications

(12 citation statements)

References 136 publications

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“…Then, the EOF‐modes of model data were projected onto the modes of the observed dataset to estimate the variance that the reference EOF‐modes explain in this projected dataset. The applicability of this method for model performance evaluation has been demonstrated in several studies (e.g., Wang and Dommenget, 2016; Lee et al ., 2018; Marathe et al ., 2021; Wang et al ., 2021). A brief mathematical description of this method is summarized hereunder.…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies have discussed the CMIP model skill in simulating the observed regional and large‐scale modes of climate variability (Zhou et al ., 2009; Xavier et al ., 2010; Bayr and Dommenget, 2014; Wang et al ., 2015; Wang and Dommenget, 2016; Lee et al ., 2018; Zhou et al ., 2018; Marathe et al ., 2021; Wang et al ., 2021; Torsri et al ., 2022). Over the EA region, CMIP models have shown reasonable skill in simulating the spatial patterns of the observed modes of rainfall variability.…”

Section: Introductionmentioning

confidence: 99%

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Coupled Model Intercomparison Project Phase 6 simulations of the spatial structure of rainfall variability over East Africa: Evaluation and projection

Mbigi

Onyango

Mtewele

et al. 2022

Intl Journal of Climatology

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Modifications in rainfall patterns may have significant effects on a variety of natural and human systems. This study evaluates the ability of 20 Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate the interannual variability of rainfall over East Africa (EA) using a method based on the empirical orthogonal function (EOF) analysis. The future changes in rainfall variability during the near (2021-2040), middle (2041-2060) and late (2080-2099) future are analysed under two different shared socioeconomic pathways (SSP), SSP2-4.5 and SSP5-8.5. Results reveal that most models captured better spatial climatological rainfall pattern than simulated amplitude in the EA region receiving bimodal rainfall pattern (EABM) compared to that with unimodal rainfall regime (EAUM) in the historical period. An ensemble mean of all models (AMME) and a set of 13 models that best simulated the rainfall variability in the base period were selected using a robust method based on the EOF analysis for further analysis. Most of the selected models and their ensemble mean (BMME) displayed good capability in representing the annual standard deviation (SD) in recent decades, whereas BMME corroborates AMME, particularly over the EABM and EAUM regions. Based on these findings, the AMME and BMME were used to evaluate the future changes in rainfall variability. The models project a significant increase in rainfall variability during March by the mid and late 21st century over the EAUM region under SSP5-8.5, whereas the increase appears much earlier in the near-future over the EABM region. In all future periods and SSPs, SD demonstrates a considerable increase over most of the EABM region, and the magnitude gradually increases from the AMME to BMME projections. Moreover, a relatively stronger increase is anticipated to actualize by the mid of 21st century.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coupled Model Intercomparison Project Phase 6 simulations of the spatial structure of rainfall variability over East Africa: Evaluation and projection

Mbigi

Onyango

Mtewele

et al. 2022

Intl Journal of Climatology

View full text Add to dashboard Cite

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“…Taiwan's climate change research capabilities are already quite competitive at the international level. For example, the Taiwan Earth System Model (TaiESM) constructed by the Academia Sinica (Lee et al, 2020; Wang et al, 2021) ranks 8th among 37 models worldwide in terms of its comprehensive simulation and projection performance. It is comparable to the model (MRI‐ESM2.0) of the Meteorological Research Institute, Japan Meteorological Agency, and takes a leading position in Asia (Ministry of Science and Technology, 2020; Program for Climate Model Diagnosis and Intercomparison, 2022).…”

Section: Policy Implicationsmentioning

confidence: 99%

How does the research community contribute to corporate climate‐related risk disclosures? The gap between ideals and reality

Lee

Kuo

Hsu

et al. 2022

Corp Soc Responsibility Env

Self Cite

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Since the inception of the Task Force on Climate Related Financial Disclosures (TCFD) and its publication of a series of guidelines to instruct companies how to respond to climate change, physical and transition risks have become must-know terms for businesses around the world. The development of various physical and transition risk assessment tools has become an urgent task for not only climate service companies but also for the scientific research community. Nevertheless, there is still an obvious gap between the contribution of the scientific research community and enterprise needs for clarification of the TCFD requirements. This article draws on interviews from representatives of more than a dozen related corporations and institutions in Taiwan, including private firms, research units, and industry associations, to understand and summarize their experience and expectations for the research community's contributions to TCFD alignment. The interview findings point out that physical risk assessment, transition risk assessment, data/information disclosure and integration, and policies and systems are the most concerned aspects of the interviewees. Based on these findings, the paper then provides suggestions for improvement by the research community, policy makers, and decision makers, including appropriate policies and systems, localized high-quality climate services, and international competition, helping to effectively bridge the gap between daily research work and external expectations for the research results.

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“…In order to realize a complex discrete dynamic system instead of manual calculation, we must first solve the problems in data. In recent years, the purpose of data enhancement processing can be achieved by integrating boosting algorithms in the process of complex discrete dynamic modeling [12]. e key content is to add learning skills to the system, so as to achieve accurate data analysis.…”

Section: Related Workmentioning

confidence: 99%

Research on Audit Simulation of Accounting Computerization Based on Internet Complex Discrete Dynamic Modeling Technology

Chen

Wang

2022

Mathematical Problems in Engineering

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With the rapid development of science and technology, modeling technology involves more and more fields and the data objects studied are more and more abstract. Based on the Internet plus complex discrete dynamic modeling technology, this paper studies the auditing and control of computerized accounting computerization. In order to maintain the steady state of the discrete dynamic system, the lifting method boosting, least square method, gradient boost, and LS-Ensem algorithm are added in the modeling process. Then, in order to improve the data recognition ability of the system, the recognition algorithm is added. The results show that the complex discrete dynamic system composed of the above algorithms is different from the ordinary system and can be better suitable for small-scale data. It also improves the stability of the whole system and then improves the accuracy of the model. Compared with ordinary discrete systems, the complex discrete dynamic systems studied in this paper can better overcome external factors and avoid errors. The system can quickly analyze and process the data that need statistics and has good application value.

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Performance of the Taiwan Earth System Model in Simulating Climate Variability Compared With Observations and CMIP6 Model Simulations

Cited by 48 publications

References 136 publications

Coupled Model Intercomparison Project Phase 6 simulations of the spatial structure of rainfall variability over East Africa: Evaluation and projection

Coupled Model Intercomparison Project Phase 6 simulations of the spatial structure of rainfall variability over East Africa: Evaluation and projection

How does the research community contribute to corporate climate‐related risk disclosures? The gap between ideals and reality

Research on Audit Simulation of Accounting Computerization Based on Internet Complex Discrete Dynamic Modeling Technology

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