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Dynamical Feedback between Synoptic Eddy and Low-Frequency Flow as Simulated by BCC_CSM1.1(m)

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Advances in Atmospheric Sciences 2017年第11期34卷 1316-1332页

作者： Fang ZHOU hong-li ren Laboratory for Climate Studies National Climate CenterChina Meteorological Administrationbeijing 100081china CMA-NJU Joint Laboratory for Climate Prediction Studies School of Atmospheric SciencesNanjing Universitynanjing 210093china Department of Atmospheric Science School of Environmental StudiesChina University of Geosciencewuhan 430074china

Since the interaction between atmospheric synoptic eddy （SE）（2-8 days） activity and low-frequency （LF）（monthly） flow （referred to as SELF） plays an essential role in generating and maintaining dominant climate modes, an evaluation of the performance of BCC_CSMI.I（m） in simulating the SE feedback onto the LF flow is given in this paper. The model captures well the major spatial features of climatological eddy vorticity forcing, eddy-induced growth rate, and patterns of SELF feedback for the climate modes with large magnitudes in cold seasons and small magnitudes in warm seasons for both the Northern and Southern Hemisphere. As in observations, the eddy-induced growth rate and SELF feedback patterns in the model also show positive SE feedback. Overall, the relationships between SE and LF flow show that BCC_CSM1. l（m） satisfactorily captures the basic features of positive SE feedback, which demonstrates the simulation skill of the model for LF variability. Specifically, such an evaluation can help to find model biases of BCC_CSM1.1 （m） in simulating SE feedback, which will provide a reference for the model＇s application.

关键词： model evaluation synoptic eddy feedback simulation eddy vorticity forcing eddy-induced growth rate patternsof synoptic eddy feedback

Diagnosing SST Error Growth during ENSO Developing Phase in the BCC_CSM1.1(m) Prediction System

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Advances in Atmospheric Sciences 2022年第3期39卷 427-442页

作者： Ben TIAN hong-li ren Laboratory for Climate Studies&CMA-NJU Joint Laboratory for Climate Prediction Studies National Climate CenterChina Meteorological AdministrationBeijing 100081China State Key Laboratory of Severe Weather Institute of Tibetan Plateau&Polar MeteorologyChinese Academy of Meteorological SciencesBeijing 100081China Department of Atmospheric Science School of Environmental StudiesChina University of GeoscienceWuhan 430074China

In this study, the predictability of the El Nino-South Oscillation(ENSO) in an operational prediction model from the perspective of initial errors is diagnosed using the seasonal hindcasts of the Beijing Climate Center System Model,BCC;SM1.1(m). Forecast skills during the different ENSO phases are analyzed and it is shown that the ENSO forecasts appear to be more challenging during the developing phase, compared to the decay phase. During ENSO development, the SST prediction errors are significantly negative and cover a large area in the central and eastern tropical Pacific, thus limiting the model skill in predicting the intensity of El Nino. The large-scale SST errors, at their early stage, are generated gradually in terms of negative anomalies in the subsurface ocean temperature over the central-western equatorial Pacific,featuring an error evolutionary process similar to that of El Nino decay and the transition to the La Nina growth ***, for short lead-time ENSO predictions, the initial wind errors begin to play an increasing role, particularly in linking with the subsurface heat content errors in the central-western Pacific. By comparing the multiple samples of initial fields in the model, it is clearly found that poor SST predictions of the Nino-3.4 region are largely due to contributions of the initial errors in certain specific locations in the tropical Pacific. This demonstrates that those sensitive areas for initial fields in ENSO prediction are fairly consistent in both previous ideal experiments and our operational predictions,indicating the need for targeted observations to further improve operational forecasts of ENSO.

关键词： ENSO prediction initial errors error evolution SST

ENSO Features, Dynamics, and Teleconnections to East Asian Climate as Simulated in CAMS-CSM

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Journal of Meteorological Research 2019年第1期33卷 46-65页

作者： Bo LU hong-li ren Laboratory for Climate Studies & China Meteorological Administration–Nanjing University Joint Laboratory for Climate Prediction Studies National Climate Center China Meteorological Administration Xinjiang Climate Center Department of Atmospheric Science School of Environmental Studies China University of Geoscience

This study evaluates the performance of CAMS-CSM(the climate system model of the Chinese Academy of Meteorological Sciences) in simulating the features, dynamics, and teleconnections to East Asian climate of the El Ni?o–Southern Oscillation(ENSO). In general, fundamental features of ENSO, such as its dominant patterns and phase-locking features, are reproduced well. The two types of El Ni?o are also represented, in terms of their spatial distributions and mutual independency. However, the skewed feature is missed in the model and the simulation of ENSO is extremely strong, which is found—based on Bjerknes index assessment—to be caused by underestimation of the shortwave damping effect. Besides, the modeled ENSO exhibits a regular oscillation with a period shorter than observed. By utilizing the Wyrtki index, it is suggested that this periodicity bias results from an overly quick phase transition induced by feedback from the thermocline and zonal advection. In addition to internal dynamics of ENSO,its external precursors—such as the North Pacific Oscillation with its accompanying seasonal footprinting mechanism, and the Indian Ocean Dipole with its 1-yr lead correlation with ENSO—are reproduced well by the model. Furthermore, with respect to the impacts of ENSO on the East Asian summer monsoon, although the anomalous Philippine anticyclone is reproduced in the post-El Ni?o summer, it exhibits an eastward shift compared with observation;and as a consequence, the observed flooding of the Yangtze River basin is poorly represented, with unrealistic air–sea interaction over the South China Sea being the likely physical origin of this bias. The response of wintertime lowertropospheric circulation to ENSO is simulated well, in spite of an underestimation of temperature anomalies in central China. This study highlights the dynamic processes that are key for the simulation of ENSO, which could shed some light on improving this model in the future.

关键词： model evaluation ENSO dynamics teleconnection CAMS-CSM

Representation of the Madden–Julian Oscillation in CAMS-CSM

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Journal of Meteorological Research 2019年第4期33卷 627-650页

作者： Pengfei ren li GAO hong-li ren Xinyao RONG Jian li Chinese Academy of Meteorological Sciences China Meteorological AdministrationBeijing 100081 Numerical Prediction Center National Meteorological CenterChina Meteorological AdministrationBeijing 100081 Laboratory for Climate Studies&China Meteorological Administration-Nanjing University Joint Laboratory for Climate Prediction Studies National Climate CenterChina Meteorological AdministrationBeijing 100081 Department of Atmospheric Sciences School of Environment StudiesChina University of GeoscienceWuhan 430074 State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081 Institute of Climate System(Polar Meteorology) Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081

The Madden–Julian Oscillation(MJO)has a significant impact on global weather and climate and can be used as a predictability resource in extended-term *** evaluate the ability of the Chinese Academy of Meteorological Sciences Climate System Model(CAMS-CSM)to represent the MJO by using the diagnostic method proposed by the US Climate Variability and Predictability Program(CliVAR)MJO Working Group(MJOWG).In general,the model simulates some major characteristics of MJO well,such as the seasonality characteristics and geographical dependence,the intensity of intraseasonal variability(ISV),dominant periodicity,propagation characteristics,coherence between outgoing longwave radiation(OLR)and wind,and life cycle of MJO ***,there are a few biases in the model when compared with observational/reanalyzed *** include an overestimate of precipitation in the convergence zone of the North and South Pacific,a slightly weaker eastward propagation,and a shift in the dominant periodicity toward lower frequencies with slower speeds of eastward *** model gives a poor simulation of the northward propagation of MJO in summer and shows less coherence between the MJO convection and *** role of moistening in the planetary boundary layer(PBL)in the eastward/northward propagation of MJO was also *** accurate representation of the vertical titling structure of moisture anomalies in CAMS-CSM leads to moistening of the PBL ahead of convection,which accounts for the eastward/northward propagation of *** simulation of the vertical structure of the wind and moisture anomalies in the western Pacific leads to a poor simulation of the northward propagation of MJO in this *** analysis of the PBL integral moisture anomalies shows that the model gives a good simulation of the moisture charging process ahead of MJO convection and that the zonal advection of moisture convergence term has a primary role in the detour of MJO over the Maritim

关键词： Madden-Julian Oscillation CAMS-CSM simulations budget analysis

Enlightenments from researches and predictions of 2014–2016 super El Nio event

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Science China Earth Sciences 2017年第9期60卷 1569-1571页

作者： Mu MU hong-li ren Institute of Atmospheric Sciences Fudan University Laboratory for Climate Studies National Climate CenterChina Meteorological Administration

El Nio is a remarkable climate phenomenon with a basinwide warming of sea surface temperatures（SST） in the easterncentral tropical Pacific. El Nio means The little Boy, or Christ Child in Spanish, and on the con... 详细信息

关键词：厄尔尼诺事件热带太平洋预测气候变暖现象海洋表面温度太平洋海温全流域西班牙

Detecting Primary Precursors of January Surface Air Temperature Anomalies in China

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Journal of Meteorological Research 2017年第6期31卷 1096-1108页

作者： Guirong TAN hong-li ren Haishan CHEN Qinglong YOU Key Laboratory of Meteorological Disaster of Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science &Technology Laboratory for Climate Studies/CMA–NJU Joint Laboratory for Climate Prediction Studies National Climate CenterChina Meteorological Administration Department of Atmospheric Science School of Environmental Studies China University of Geoscience

This study aims to detect the primary precursors and impact mechanisms for January surface temperature anomaly （JSTA） events in China against the background of global warming, by comparing the causes of two extreme JSTA events occurring in 2008 and 2011 with the common mechanisms inferred from all typical episodes during 1979- 2008. The results show that these two extreme events exhibit atmospheric circulation patterns in the mid-high latitudes of Eurasia, with a positive anomaly center over the Ural Mountains and a negative one to the south of Lake Baikal （UMLB）, which is a pattern quite similar to that for all the typical events. However, the Eurasian teleconnection patterns in the 2011 event, which are accompanied by a negative phase of the North Atlantic Oscillation, are different to those of the typical events and the 2008 event. We further find that a common anomalous signal appearing in early summer over the tropical Indian Ocean may be responsible for the following late-winter Eurasian teleeonnec- lions and the associated JSTA events in China. We show that sea surface temperature anomalies （SSTAs） in the preceding summer over the western Indian Ocean （WIO） are intimately related to the UMLB-like circulation pattern in the following January. Positive WIOSSTAs in early summer tend to induce strong UMLB-like circulation anomalies in January, which may result in anomalously or extremely cold events in China, which can also be successfully reproduced in model experiments. Our results suggest that the WIOSSTAs may be a useful precursor for predicting JSTA events in China.

关键词： Eurasian teleconnection anomalous temperature in China seasonal climate prediction SST anomaly western Indian Ocean model experiment

Indian Ocean SST modes and Their Impacts as Simulated in BCC_CSM1.1(m) and HadGEM3

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Advances in Atmospheric Sciences 2018年第8期35卷 139-152页

作者： Bo LU hong-li ren Rosie EADE Martin ANDREWS Laboratory for Climate Studies National Climate Center China Meteorological Administration Beijing 100081 China CMA-NJU Joint Laboratory for Climate Prediction Studies Institute for Climate and Global Change Research School of Atmospheric Sciences Nanjing University Nanjing 210023 China Xin Jiang Climate Center Urumqi 830002 China Department of Atmospheric Science School of Environmental Studies China University of Geoscience Wuhan 430074 China Met Office Hadley Center Exeter EX1 3PB UK

The sea surface temperature anomalies （SSTAs） in the tropical Indian Ocean （TIO） show two dominant modes at interan- nual time scales, referred to as the Indian Ocean basin mode （IOBM） and dipole mode （IOD）. Recent studies have shown that the IOBM and IOD not only affect the local climate, but also induce remarkable influences in East Asia via teleconnections. In this study, we assess simulations of the IOBM and IOD, as well as their teleconnections, using the operational seasonal pre- diction models from the Met Office （HadGEM3） and Beijing Climate Center [BCC_CSMI.I（m）]. It is demonstrated that the spatial patterns and seasonal cycles axe generally reproduced by the control simulations of BCC_CSM1.1 （m） and HadGEM3, although spectra biases exist. The relationship between the TIO SSTA and E1 Nifio is successfully simulated by both mod- els, including the persistent IOBM warming following E1 Nifio and the IOD-E1 Nifio interactions. BCC_CSMI.I（m） and HadGEM3 axe capable of simulating the observed local impact of the IOBM, such as the strengthening of the South Asian high. The influences of the IOBM on Yangtze River rainfall are also captured well by both models, although this teleconnec- tion is slightly weaker in BCC_CSM 1.1 （m） due to the underestimation of the northwestern Pacific subtropical high. The local effect of the IOD on East African rainfall is reproduced by both models. However, the remote control of the IOD on rainfall over southwestern China is not clear in either model. It is shown that the realistic simulations of TIO SST modes and their teleconnections give rise to the source of skillful seasonal predictions over China.

关键词： Indian Ocean SST teleconnection simulation seasonal prediction

Dynamical and Machine Learning Hybrid Seasonal Prediction of Summer Rainfall in China

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Journal of Meteorological Research 2021年第4期35卷 583-593页

作者： Jialin WANG Jing YANG hong-li ren Jinxiao li Qing BAO Miaoni GAO State Key Laboratory of Earth Surface Process and Resource Ecology/Key Laboratory of Environmental Change and Natural Disaster Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875 Southern Marine Science and Engineering Guangdong Laboratory Guangzhou 511458 State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029 Institute for Disaster Risk Management School of Geographical SciencesNanjing University of Information Science&TechnologyNanjing 210044

Seasonal prediction of summer rainfall is crucial to reduction of regional disasters,but currently it has a low prediction *** developed a dynamical and machine learning hybrid(MLD)seasonal prediction method for summer rainfall in China based on circulation fields from the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2)operational dynamical prediction *** selecting optimum hyperparameters for three machine learning methods to obtain the best fit and least overfitting,an ensemble mean of the random forest and gradient boosting regression tree methods was shown to have the highest prediction skill measured by the anomalous correlation *** skill has an average value of 0.34 in the historical cross-validation period(1981-2010)and 0.20 in the 10-yr period(2011-2020)of independent prediction,which significantly improves the dynamical prediction skill by 400%.Both reducing overfitting and using the best dynamical prediction are important in applications of the MLD method and in-depth analysis of these warrants a further investigation.

关键词： seasonal rainfall prediction statistical-dynamical model machine learning

Improvement of Soil Moisture Simulation in Eurasia by the Beijing Climate Center Climate System Model from CMIP5 to CMIP6

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Advances in Atmospheric Sciences 2021年第2期38卷 237-252页

作者： Yinghan SANG hong-li ren Xueli SHI Xiaofeng XU Haishan CHEN School of Atmospheric Sciences Nanjing University of Information Science&TechnologyNanjing 210044China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081China Laboratory for Climate Studies&CMA-NJU Joint Laboratory for Climate Prediction Studies National Climate CenterChina Meteorological AdministrationBeijing 100081China China Meteorological Administration Beijing 100081China

This study provides a comprehensive evaluation of historical surface soil moisture simulation(1979-2012)over Eurasia at annual and seasonal time scales between two medium-resolution versions of the Beijing Climate Center Climate System Model(BCC-CSM)—one that is currently participating in phase 6 of the Coupled Model Intercomparison Project(CMIP6),i.e.,BCC-CSM2-MR,and the other,BCC-CSM1.1m,which participated in *** show that BCC-CSM2-MR is more skillful in reproducing the climate mean states and standard deviations of soil moisture,with pattern correlations increased and biases reduced ***-CSM2-MR performs better in capturing the first two primary patterns of soil moisture anomalies,where the period of the corresponding time series is closer to that of reference *** show that BCC-CSM2-MR performs at a high level among multiple models of CMIP6 in terms of centered pattern correlation and“amplitude of variation”(relative standard deviation).In general,the centered pattern correlation of BCC-CSM2-MR,ranging from 0.61 to 0.87,is higher than the multi-model mean of CMIP6,and the relative standard deviation is 0.75,which surmounts the overestimations in most of the CMIP6 *** to the vital role played by precipitation in land-atmosphere interaction,possible causes of the improvement of soil moisture simulation are further related to precipitation in *** results indicate that a better description of the relationship between soil moisture and precipitation and a better reproduction of the climate mean precipitation by the model may result in the improved performance of soil moisture simulation.

关键词： BCC-CSM soil moisture CMIP6 historical simulation Eurasia