Global carbon budgets simulated by the Beijing Climate Center Climate System Model for the last century

Wu, Tongwen; Li, Weiping; Ji, Jinjun; Xin, Xiaoge; Li, Laurent; Wang, Zaizhi; Zhang, Yanwu; Li, Jianglong; Zhang, Fang; Wang, Min; Shi, Xueli; Wu, Fang; Zhang, Li; Chu, Min; Wei, Jie; Liu, Yiming; Wang, Fang; Liu, Xiangwen; Li, Qiaoping; Dong, Ming; Liang, Xiaoyan; Gao, Yang; Zhang, Jie

doi:10.1002/jgrd.50320

Cited by 216 publications

(96 citation statements)

References 113 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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“…The RCM outputs used in this study are based on the work done by Gao et al (2013). In Gao et al (2013), the RCM model (RegCM; Giorgi and Mearns, 1999) was driven by a global climate model BCC_CSM1.1 (Beijing Climate Center Climate System Model; Wu et al, 2013;Xin et al, 2013) at a horizontal resolution of 50 km over China.…”

Section: Simulated Meteorological Variables From the Rcmmentioning

confidence: 99%

Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China

Fang

Jian

Chen

et al. 2015

Hydrol. Earth Syst. Sci.

374

154

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Abstract. Water resources are essential to the ecosystem and social economy in the desert and oasis of the arid Tarim River basin, northwestern China, and expected to be vulnerable to climate change. It has been demonstrated that regional climate models (RCMs) provide more reliable results for a regional impact study of climate change (e.g., on water resources) than general circulation models (GCMs). However, due to their considerable bias it is still necessary to apply bias correction before they are used for water resources research. In this paper, after a sensitivity analysis on input meteorological variables based on the Sobol' method, we compared five precipitation correction methods and three temperature correction methods in downscaling RCM simulations applied over the Kaidu River basin, one of the headwaters of the Tarim River basin. Precipitation correction methods applied include linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), distribution mapping (DM) and quantile mapping (QM), while temperature correction methods are LS, variance scaling (VARI) and DM. The corrected precipitation and temperature were compared to the observed meteorological data, prior to being used as meteorological inputs of a distributed hydrologic model to study their impacts on streamflow. The results show (1) streamflows are sensitive to precipitation, temperature and solar radiation but not to relative humidity and wind speed; (2) raw RCM simulations are heavily biased from observed meteorological data, and its use for streamflow simulations results in large biases from observed streamflow, and all bias correction methods effectively improved these simulations; (3) for precipitation, PT and QM methods performed equally best in correcting the frequency-based indices (e.g., standard deviation, percentile values) while the LOCI method performed best in terms of the time-series-based indices (e.g., Nash-Sutcliffe coefficient, R 2 ); (4) for temperature, all correction methods performed equally well in correcting raw temperature; and (5) for simulated streamflow, precipitation correction methods have more significant influence than temperature correction methods and the performances of streamflow simulations are consistent with those of corrected precipitation; i.e., the PT and QM methods performed equally best in correcting flow duration curve and peak flow while the LOCI method performed best in terms of the time-series-based indices. The case study is for an arid area in China based on a specific RCM and hydrologic model, but the methodology and some results can be applied to other areas and models.

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“…It spans from several weeks for the atmosphere (e.g., Phillips et al, 2004) to several centuries for ocean and sea ice components (e.g., Stouffer et al, 2004). The equilibration of ocean biogeochemical tracers across the entire water column amounts to several thousands of years (e.g., Heinze et al, 1999;Wunsch and Heimbach, 2008) and depends on the state of background ocean circulation as well as the turbulent mixing and eddy stirring parameterizations (e.g., Aumont et al, 1998;Bryan, 1984;Marinov et al, 2008). The equilibration time can be different in a coupled model configuration (i.e., ocean-atmosphere general circulation models or ESMs) compared to stand-alone climate components due to leaks in the energy budget (Hobbs et al, 2016).…”

Section: Initialization Of Biogeochemical Fields and Spin-up Protocolmentioning

confidence: 99%

Inconsistent strategies to spin up models in CMIP5: implications for ocean biogeochemical model performance assessment

et al. 2016

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Abstract. During the fifth phase of the Coupled Model Intercomparison Project (CMIP5) substantial efforts were made to systematically assess the skill of Earth system models. One goal was to check how realistically representative marine biogeochemical tracer distributions could be reproduced by models. In routine assessments model historical hindcasts were compared with available modern biogeochemical observations. However, these assessments considered neither how close modeled biogeochemical reservoirs were to equilibrium nor the sensitivity of model performance to initial conditions or to the spin-up protocols. Here, we explore how the large diversity in spin-up protocols used for marine biogeochemistry in CMIP5 Earth system models (ESMs) contributes to model-to-model differences in the simulated fields. We take advantage of a 500-year spin-up simulation of IPSL-CM5A-LR to quantify the influence of the spin-up protocol on model ability to reproduce relevant data fields. Amplification of biases in selected biogeochemical fields (O 2 , NO 3 , Alk-DIC) is assessed as a function of spin-up duration. We demonstrate that a relationship between spin-up duration and assessment metrics emerges from our model results and holds when confronted with a larger ensemble of CMIP5 models. This shows that drift has implications for performance assessment in addition to possibly aliasing estimates of climate change impact. Our study suggests that differences in spin-up protocols could explain a substantial part of model disparities, constituting a source of modelto-model uncertainty. This requires more attention in future Published by Copernicus Publications on behalf of the European Geosciences Union. R. Séférian et al.: Inconsistent strategies to spin up models in CMIP5model intercomparison exercises in order to provide quantitatively more correct ESM results on marine biogeochemistry and carbon cycle feedbacks.

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Global carbon budgets simulated by the Beijing Climate Center Climate System Model for the last century

Cited by 216 publications

References 113 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

Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China

Inconsistent strategies to spin up models in CMIP5: implications for ocean biogeochemical model performance assessment

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