Evaluation of the performance of the Beijing Climate Centre Climate System Model 1.1(m) to simulate precipitation across China based on long‐range correlation characteristics

Zhao, Shanshan; He, Wenping

doi:10.1002/2015jd024059

Cited by 19 publications

(12 citation statements)

References 44 publications

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“…In this study, DFA2 is used (Kantelhardt et al, 2001). To estimate the uncertainties of the DFA2 method, we conducted six sets of independent tests for six given scaling exponents according to Zhao and He (2015b). In each test, Fourier-filtering method (FFM, Peng et al, 1991) produced 20 000 artificial time series with given scaling exponents.…”

Section: Detrended Fluctuation Analysismentioning

confidence: 99%

“…The LRC of a climate system is the theoretical basis for long-term climate predictions (Huang et al, 1993;Dong, 2003, 2004;Ding and Li, 2007;Li and Wang, 2008;Ding, 2011, 2013). Detrended fluctuation analysis (DFA) is a nonlinear approach to detect LRC in time series based on random processes and chaos theory (Peng et al, 1994); it has been used to evaluate the dynamic characteristics of climate system models (Blender and Fraedrich, 2003;Zhao and He, 2014, 2015a, 2015b. In 2002, Govindan et al (2002) found that seven leading global climate models (GCMs) fail to reproduce the universal scaling behaviour of the observed temperature records for two different scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of NCEP‐2 and CFSR reanalysis seasonal temperature data in China using detrended fluctuation analysis

Zhao

Jiang

2017

Intl Journal of Climatology

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ABSTRACT:In the present study, the performance of the National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) Atmospheric Model Intercomparison Project (AMIP-II) reanalysis (NCEP-2) and NCEP Climate Forecast System Reanalysis (CFSR) seasonal temperature data for China was quantitatively evaluated by using detrended fluctuation analysis. The results indicate that the quality of the NCEP-2 temperature data is the highest in autumn, while the quality of CFSR data set is the highest in summer. Both the quality of NCEP-2 and that of CFSR temperature data is the lowest in winter. The quality is very low in eastern Tibetan Plateau and most of Xinjiang at a significance level of 0.05 for the NCEP-2 and CFSR reanalysis temperature data including three elements: the daily average temperature (T mean ), the daily maximum temperature (T max ) and the daily minimum temperature (T min ). The performance of the NCEP-2 T mean data is poorer than those of the NCEP-2 T max and T min data for all four seasons, while the performance of CFSR T mean is better than that of CFSR T max and T min for all four seasons. In addition, the NCEP-2 T max , T min and CFSR T mean , T max data perform well in most of central and eastern China for all four seasons. The quality of the CFSR T mean is better than that of the NCEP-2 T mean for all four seasons. However, the NCEP-2 T max and T min data have higher credibility than that of the CFSR T max and T min data for all four seasons. Therefore, it is important to consider the reliability of the NCEP-2 and CFSR daily temperature reanalysis data in different districts and seasons when drawing conclusions from the different reanalysis data.KEY WORDS detrended fluctuation analysis; scaling exponent; long-range correlation; quality of reanalysis data

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Section: Detrended Fluctuation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of NCEP‐2 and CFSR reanalysis seasonal temperature data in China using detrended fluctuation analysis

Zhao

Jiang

2017

Intl Journal of Climatology

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“…BCC-CSM2 is a fully coupled global CSM in which the atmosphere, land, ocean, and sea-ice components interact with each other through the exchange of momentum, energy, water, and carbon (Wu et al, 2008(Wu et al, , 2013(Wu et al, , 2019. The earlier version of BCC-CSM (v1.1 and v1.1m) was enrolled in CMIP5 and has been widely applied on weather and climate research (e.g., Wu et al, 2013Wu et al, , 2014Xin et al, 2013;Zhao and He, 2015). It has been recently updated to BCC-CSM2 and is being used in that configuration for the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Wu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Development of the global atmospheric general circulation-chemistry model BCC-GEOS-Chem v1.0: model description and evaluation

Zhang

et al. 2019

Preprint

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Abstract. Chemistry plays an indispensable role in investigations of the atmosphere, however, many climate models either ignore or greatly simplify atmospheric chemistry, limiting both their accuracy and their scope. We present the development and evaluation of the online global atmospheric chemical model BCC-GEOS-Chem v1.0, coupling the GEOS-Chem chemical transport model (CTM) as an atmospheric chemistry component in the Beijing Climate Center atmospheric general circulation model (BCC-AGCM). The GEOS-Chem atmospheric chemistry component includes detailed tropospheric HOx-NOx-VOC-ozone-bromine-aerosol chemistry and online dry and wet deposition schemes. We then demonstrate the new capabilities of BCC-GEOS-Chem v1.0 relative to the base BCC-AGCM model through a three-year (2012–2014) simulation with anthropogenic emissions from the Community Emissions Data System (CEDS) used in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The model well captures the spatial distributions and seasonal variations in tropospheric ozone, with seasonal mean biases of 0.4–2.2 ppbv at 700–400 hPa compared to satellite observations and within 10 ppbv at the surface-500 hPa compared to global ozonesonde observations. The model has larger high ozone biases over the tropics which we attribute to an overestimate of ozone chemical production. It underestimates ozone in the upper troposphere which likely due to either the use of a simplified stratospheric ozone scheme and/or to biases in estimated stratosphere-troposphere exchange dynamics. The model diagnoses the global tropospheric ozone burden, OH concentration, and methane chemical lifetime to be 336 Tg, 1.16 × 106 molecule cm−3, and 8.3 years, respectively, consistent with recent multi-model assessments. The spatiotemporal distributions of NO2, CO, SO2, CH2O, and aerosols optical depth are generally in agreement with satellite observations. The development of BCC-GEOS-Chem v1.0 represents an important step for the development of fully coupled earth system models (ESMs) in China.

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“…Due to its advantages and applicability (Bashan et al 2008), this method has been widely used in the meteorological field in recent years (Kantelhardt et al 2006;Fraedrich et al 2009;Zhu et al 2010;Bunde et al 2013;Jiang et al 2015;Yuan et al 2015;He et al 2016;Jiang et al 2017). With the extensive usage of climate models, people begin to use the DFA method to assess merits of climate models by comparing the difference of scaling exponents between the simulated data of climate models and observed data (Govindan et al 2002;Zhao and He 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In 2015, Zhao and He produced 20000 artificial series with the given scaling exponents by Fourier Filtering in order to test the uncertainties of DFA method. Results of the artificial series indicate that the uncertainties of DFA method range from −0.05 to 0.05 for the precipitation series (Zhao and He 2015). For convenience, this article defines the deviation ranging from −0.05 to 0.05 as the 0.1 significant test level of precipitation series over the Qinghai-Tibet Plateau.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Daily Precipitation from CMIP5 in the Qinghai-Tibet Plateau

Zhong-ke

Ren

Miao

et al. 2019

SOLA

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As the earth's third pole, Qinghai-Tibet Plateau belongs to one of the most sensitive regions to climate change in the world. Based on the observed and the simulated daily precipitation from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we evaluated the simulation performance of daily precipitation from selected CMIP5 models from 1975 to 2005 over the Qinghai-Tibet Plateau. We found that daily precipitation exhibited obvious longrange correlation characteristics using the detrended fluctuation analysis method. The scaling exponents of daily precipitation in summer and autumn are significantly larger than those in spring and winter. MIROC4H with the best performance can reproduce long-range correlation characteristic of daily precipitation series probably because of the higher resolution, which can capture small scale cloud convections. Besides there are seasonal differences in the simulation results among different regions of the Qinghai-Tibet Plateau, simulation effects of all climate models in summer and winter are better than those in spring and autumn. The performance of MIROC4H model works the best in spring. Overall, the scaling exponents of daily precipitation from BCC-CSM1-1-M, CMCC-CM and MIROC4H are close to the observations. CCSM4 and MIROC4H climate models could reproduce the internal dynamics characteristic of daily precipitation in autumn. But for winter, all climate models have exaggerated the scaling value in southeastern Qinghai-Tibet Plateau compared with the observed values.

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Evaluation of the performance of the Beijing Climate Centre Climate System Model 1.1(m) to simulate precipitation across China based on long‐range correlation characteristics

Cited by 19 publications

References 44 publications

Evaluation of NCEP‐2 and CFSR reanalysis seasonal temperature data in China using detrended fluctuation analysis

Evaluation of NCEP‐2 and CFSR reanalysis seasonal temperature data in China using detrended fluctuation analysis

Development of the global atmospheric general circulation-chemistry model BCC-GEOS-Chem v1.0: model description and evaluation

Simulation of Daily Precipitation from CMIP5 in the Qinghai-Tibet Plateau

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