An overview of BCC climate system model development and application for climate change studies

Wu, Tongwen; Song, Lianchun; Li, Weiping; Wang, Zaizhi; Zhang, Huanhuan; Xin, Xiaoge; Zhang, Yanwu; Zhang, Li; Li, Jianglong; Wu, Fang; Liu, Yiming; Zhang, Fang; Shi, Xueli; Chu, Min; Zhang, Jie; Fang, Yongjie; Wang, Fang; Lu, Yixiong; Liu, Xiangwen; Wang, Min; Liu, Qianxia; Zhou, Wenyan; Dong, Ming; Zhao, Qiudong; Ji, Jinjun; Li, Laurent; Zhou, Mingyu

doi:10.1007/s13351-014-3041-7

Cited by 200 publications

(129 citation statements)

References 42 publications

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“…The sea ice component is GFDL Sea Ice Simulator. Details on similar versions of BCC_CSM and their use in climate change projection and short-term climate prediction have been documented in several studies (e.g., Wu et al 2013Wu et al , 2014Liu et al 2014Liu et al , 2015.…”

Section: Modelmentioning

confidence: 99%

Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Liu

et al. 2018

Clim Dyn

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Using the sub-seasonal to seasonal forecast model of Beijing Climate Center, several key physical parameters are perturbed by the Latin hypercube sampling method to find a better configuration for representation of Madden-Julian oscillation (MJO) in the free-run simulation. We find that although model simulation is especially sensitive to some parameters, there are overall no significant linear relationships between model skill and any one of the parameters, and the optimum performance can be obtained by combined perturbations of multiple parameters. By optimization, MJO's spectrum, intensity, spatial structure and propagation, as well as the mean state and variance, are all improved to some extent, suggesting the correspondence and interrelation of model's performances in simulating different characteristics of MJO. Further, several sets of initialized hindcasts using the optimized parameters are conducted, and their results are compared with the hindcasts using only improved initial conditions. We show that with an optimized model, the forecast of MJO beyond 3-week lead time is not improved, and the maximum useful skill is only slightly increased, implying that a decrease of model error does not always translate into an increase of forecast skill at all lead time. However, the skill is obviously enhanced during lead times of 2-3 weeks for forecasts in most seasons and initial phases except for a few cases. Particularly, the deficiency in forecasting MJO's propagation from the Indian Ocean to the Pacific is relieved, further highlighting the positive contribution of reducing model error compared to previous work that only reduced initial condition error. In this study, we also show benefits of multi-scheme ensemble strategy in describing uncertainties of model error and initial condition error and thus improving MJO forecast.

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Section: Modelmentioning

confidence: 99%

Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Liu

et al. 2018

Clim Dyn

Self Cite

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“…S4). Wu et al (2014) and temperature simulations against instrumental reference data focusing on the northern European sector.…”

Section: Cmip5-pmip3 Simulationsmentioning

confidence: 99%

Hydroclimate variability in Scandinavia over the last millennium – insights from a climate model–proxy data comparison

et al. 2017

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Abstract. The integration of climate proxy information with general circulation model (GCM) results offers considerable potential for deriving greater understanding of the mechanisms underlying climate variability, as well as unique opportunities for out-of-sample evaluations of model performance. In this study, we combine insights from a new tree-ring hydroclimate reconstruction from Scandinavia with projections from a suite of forced transient simulations of the last millennium and historical intervals from the CMIP5 and PMIP3 archives. Model simulations and proxy reconstruction data are found to broadly agree on the modes of atmospheric variability that produce droughts-pluvials in the region. Despite these dynamical similarities, large differences between simulated and reconstructed hydroclimate time series remain. We find that the GCM-simulated multi-decadal and/or longer hydroclimate variability is systematically smaller than the proxy-based estimates, whereas the dominance of GCMsimulated high-frequency components of variability is not reflected in the proxy record. Furthermore, the paleoclimate evidence indicates in-phase coherencies between regional hydroclimate and temperature on decadal timescales, i.e., sustained wet periods have often been concurrent with warm periods and vice versa. The CMIP5-PMIP3 archive suggests, however, out-of-phase coherencies between the two variables in the last millennium. The lack of adequate understanding of mechanisms linking temperature and moisture supply on longer timescales has serious implications for attribution and prediction of regional hydroclimate changes. Our findings stress the need for further paleoclimate data-model intercomparison efforts to expand our understanding of the dynamics of hydroclimate variability and change, to enhance our ability to evaluate climate models, and to provide a more comprehensive view of future drought and pluvial risks.

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“…BCC_CSM1.1 completely couples the atmospheric model BCC_AGCM2.1, the land surface model BCC_AVIM1.0, the global ocean circulation model MOM4_L40, and the global dynamic/thermodynamic sea ice model SIS using a flux coupler. Detailed descriptions of these models are provided by Xu et al [34] and Xin et al [37,38]. The future climate conditions (2070s) shows that mean temperature will increase from 2 °C to 4.1 °C and annual precipitation will increase from 8 mm to 34 mm relative to 1950-2000 (7.6 °C and 441 mm) on Loess Plateau under four RCP scenarios predicted by BCC_CSM1.1.…”

Section: Current Climate Layers and Future Climate Scenariosmentioning

confidence: 99%

Simulating the Effect of Climate Change on Vegetation Zone Distribution on the Loess Plateau, Northwest China

Wen

Guo

et al. 2015

Forests

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A risk assessment of vegetation zone responses to climate change was conducted using the classical Holdridge life zone model on the Loess Plateau of Northwest China. The results show that there are currently ten vegetation zones occurring on the Loess Plateau , including alvar desert, alpine wet tundra, alpine rain tundra, boreal moist forest, boreal wet forest, cool temperate desert, cool temperate desert scrub, cool temperate steppe, cool temperate moist forest, warm temperate desert scrub, warm temperate thorn steppe, and warm temperate dry forest. Seventy years later (2070S), the alvar desert, the alpine wet tundra and the cool temperate desert will disappear, while warm temperate desert scrub and warm temperate thorn steppe will emerge. The area proportion of warm temperate dry forest will expand from 12.2% to 22.8%-37.2%, while that of cool temperate moist forest will decrease from 18.5% to 6.9%-9.5%. The area proportion of cool temperate steppe will decrease from 51.8% to 34.5%-51.6%. Our results suggest that future climate change will be conducive to the growth and expansion of forest zones on the Loess Plateau, which can provide valuable reference information for regional vegetation restoration planning and adaptive strategies in this region. OPEN ACCESSForests 2015, 6 2093

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An overview of BCC climate system model development and application for climate change studies

Cited by 200 publications

References 42 publications

Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Hydroclimate variability in Scandinavia over the last millennium – insights from a climate model–proxy data comparison

Simulating the Effect of Climate Change on Vegetation Zone Distribution on the Loess Plateau, Northwest China

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