气候与环境研究  2017, Vol. 22 Issue (5): 633-642 PDF
CMIP5模式对冬季北极涛动的模拟和预估

1 南京信息工程大学大气科学学院, 南京 210044;
2 山西省气象灾害防御技术中心, 太原 030002;
3 山西省五台山气象站, 山西五台县 035515;
4 山西省预警信息发布中心, 太原 030002;
5 山西省代县气象局, 山西代县 034200

Simulation and Projection of the Arctic Oscillation in Winter Based on CMIP5 Models
ZHAGN Yongrui1,2, LI Liping1, JIN Zehui3, LIU Pu4, KANG Xi5
1 College of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044;
2 Shanxi Province Meteorological Disasters Prevention Technology Center, Taiyuan 030002;
3 Wutai Shan Weather Station, Shanxi Province, Wutai Xian Shanxi 035515;
4 Shanxi Province Early Warning Information Release Center, Taiyuan 030002;
5 Dai Xian Meteorological Office, Shanxi Province, Dai Xian Shanxi 034200
Abstract: On the basis of the NCEP/NCAR reanalysis data and CMIP5 19 model results, this work examined the performance of 19 CMIP5 models in the simulation of temporal variability and spatial pattern of the Arctic Oscillation (AO), and estimated the future changes of AO under two typical Representative Concentration Pathway scenarios (i.e., RCP4.5 and RCP8.5). Results show that most of the models can capture the basic structure of the AO mode, while MPI-ESM-LR and HadGEM2-AO can better simulate the overall AO mode than other models. However, some models overestimate the anomalous distribution on the Pacific side. Regarding the time series and temporal variability, the first mode of principal component (PC1) of the CMIP5 models basically can reproduce the weakening trend since 1950-1970, but the growing trend after 1970 is not obvious in the simulations. Nevertheless, the zonal index (ZI) sequence can simulate the trend in the two stages. On the whole, most of the PC1 and ZI sequences during 1950-2005 show a positive trend. More than half of the CMIP5 models can well simulate the high frequency cycles with 2-3 a; however, the simulations of 20-aquasi-periodic oscillation are poor. Among all the models, only CanESM2, CNRM-CM5, and GFDL-ESM2G can better simulate the ZI cycle inversion. It is found that the ZI sequence shows a significant upward trend under the RCP4.5 and RCP8.5 scenarios, and the interdecadal variation is obvious with three different stages, i.e., 2006-2039 and 2070-2001 correspond to two rising phases, while 2040-2069 is the slow descending phase. A majority of the simulation results reveals a positive trend, and more than 10 out of the 19 CMIP5 models have passed the test under both concentration scenarios.
Key words: CMIP5 model     Winter     Arctic Oscillation     Simulation and projection
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1 引言

2 资料与方法 2.1 资料

2.2 方法

Lorenz (1951)最早指出北半球纬向平均海平面气压存在南北反向变化，Thompson and Wallace (1998)重新进行了研究，并用EOF方法提取了其指数，并命名为北极涛动，其第一模态为AO模态。然而，有些学者认为它是由EOF这种数学方法构造出来的，而不是真实存在的现象，因此，北极涛动的真实性被置疑(Kerr, 1999; Ambaum et al., 2001)。Li and Wang (2003)对AO的真实性及其物理本质进行了研究，提出北半球热带外大气环流存在两个环状活动带，一个位于副热带和中纬度地区，一个位于高纬度地区，两个环状活动带的中心分别位于35°N和65°N，两者之间的海平面气压存在反向变化结构，并依此定义了一种新的北极涛动指数，该指数利用这两个环状活动带中心纬度35°N和65°N上的标准化纬向平均海平面气压差来表征AO的强弱，其公式如下：

 ${{I}_{z}}={{\hat{P}}_{\text{35}{}^\circ \text{N}}}-{{\hat{P}}_{\text{65}{}^\circ \text{N}}},$ (1)

3 气候特征的模拟评估 3.1 北半球海平面气压异常模态

 图 1 （a）NCEP/NCAR观测数据、（b−t）CMIP5模式模拟的北半球冬季海平面气压EOF第一模态（EOF1） Fig. 1 Spatial patterns of the first Empirical Orthogonal Function (EOF) mode (EOF1) of the Dec to next Feb sea level pressure (DJF SLP) in the Northern Hemisphere from (a) NCEP /NCAR reanalysis data and (b−t) CMIP5 models

 图 2 NCEP/NCAR观测数据与CMIP5模式模拟的海平面气压EOF1的泰勒图（REF参考点代表观测资料，各模式到原点的半径代表其相对于观测值的标准差，模式在图中方位角的余弦代表模式与观测的相关系数，模式到参考点的距离代表其均方根误差） Fig. 2 Taylor diagram of the CMIP5 simulated SLP EOF1 compared to the NCEP/NCAR reanalysis data (REF indicates the reference value of 1.0; the radial distance from the model code point to the origin is the standardized deviation ratio of the model output relative to the observation. The spatial correlation coefficient between the model output and observations is shown by the cosine of the azimuthally angle of model code point, and the root-mean-square error is given by the distance from the model code point to REF)

 图 3 1950~2005年NCEP/NCAR与CMIP5模式的冬季海平面气压（a）PC1和（b）ZI的时间序列 Fig. 3 The time series of the DJF SLP (a) first principal component (PC1) and (b) zonal index (ZI) during 1950 to 2005 from CMIP5 models and NCEP /NCAR reanalysis data

 图 4 1950~2005年NCEP/NCAR观测数据与CMIP5模式模拟的冬季海平面气压（a）PC1和（b）ZI的线性趋势系数（黑色表示通过了0.05显著性检验） Fig. 4 Linear trend coefficients of the DJF SLP (a) PC1 and (b) ZI during 1950 to 2005 from NCEP/NCAR reanalysis data and CMIP5 models (the black box indicates that it is significant at the 0.05 level)
3.2 ZI的周期特征分析

 图 5 NCEP/NCAR观测数据和CMIP5模式模拟的的ZI序列功率谱（实线，两条虚线分别为0.05和0.1显著性水平对应的红噪声检验） Fig. 5 Power spectra (solid lines) of the DJF ZI from the (a) NCEP /NCAR reanalysis data and (b−t) the CMIP5 model simulations (dashed lines indicate significance at the 0.05 and 0.1 levels by red noise test)

4 未来情景下AO的可能变化趋势

 图 6 （a）RCP4.5和RCP8.5的ZI时间序列以及（b）RCP4.5、（c）RCP8.5 ZI的趋势系数（黑色表示通过了0.05显著性检验） Fig. 6 (a) Time series of ZI under RCP4.5 and RCP8.5 scenarios and linear trend coefficients of ZI under (b) RCP4.5 scenario and (c) RCP8.5 scenario (black bars indicate that the values are significant at the 0.05 level)

 图 7 相对于1981~2005年参考时段，（a−c）RCP4.5和（d−f）RCP8.5不同时段的冬季海平面气压的线性变化趋势[单位：hPa（10 a）−1] Fig. 7 Linear trends [hPa (10 a)−1] of annual DJF SLP changes under (a−c) RCP4.5 and (d−f) RCP8.5 scenarios in different stages relative to 1981−2005
5 结论和讨论