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Jinqing Zuo, Hong-Li Ren, and Weijing Li

Abstract

In the boreal winter, the Arctic Oscillation (AO) evidently acts to influence surface air temperature (SAT) anomalies in China. This study reveals a large intraseasonal variation in the relationship between the winter AO and southern China SAT anomalies. Specifically, a weak in-phase relationship occurs in December, but a significant out-of-phase relationship occurs in January and February. The authors show that the linkage between the AO and southern China SAT anomalies strongly depends on the AO-associated changes in the Middle East jet stream (MEJS) and that such an AO–MEJS relationship is characterized by a significant difference between early and middle-to-late winter. In middle-to-late winter, the Azores center of high pressure anomalies in the positive AO phase usually extends eastward and yields a significantly anomalous upper-level convergence over the Mediterranean Sea, which can excite a Rossby wave train spanning the Arabian Sea and intensify the MEJS. In early winter, however, the Azores center of the AO is apparently shifted westward and is mainly confined to the Atlantic Ocean; in this case, the associated change in the MEJS is relatively weak. Both observational diagnoses and experiments based on a linearized barotropic model suggest that the MEJS is closely linked to the AO only when the latter generates considerable upper-level convergence anomalies over the Mediterranean Sea. Therefore, the different impacts of the AO on the MEJS and the southern China SAT anomalies between early and middle-to-late winter are primarily attributed to the large intraseasonal zonal migrations of the Azores center of the AO.

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Jinqing Zuo, Hong-Li Ren, Weijing Li, and Lei Wang

Abstract

Interdecadal variations in the relationship between the winter North Atlantic Oscillation (NAO) and surface air temperature in China are investigated using observational and reanalysis data. Focus is on south-central China, in which temperature variability is strongly related to the NAO. It is revealed that the relationship shows clear interdecadal variations in midwinter during 1951–2015. A relatively weak in-phase relationship occurs before the early 1970s (P1), but a significant out-of-phase relationship dominates in the last two decades of the twentieth century (P2), though it is clearly weaker from the late 1990s onward. Observational evidence shows that such interdecadal variations are related mainly to variations in the spatial pattern and amplitude of the NAO. The northern center of the NAO shifted eastward over the second half of the twentieth century. In addition, the amplitude of the center strengthened from P1 to P2, resulting in a perturbation in the atmospheric circulation response pattern over Eurasian mid-to-high latitudes. During P2, the eastward shift and amplitude intensification of the NAO favored a north–south dipole structure in circulation anomalies over the Asian continent, which tended to produce cold temperature anomalies in south-central China during the positive NAO phase and warm anomalies during the negative phase. However, in the past two decades the northern center of the NAO has weakened and retreated westward. This was concurrent with a weakening relationship between the NAO and temperature anomalies in south-central China and northern Eurasia, indicating weaker downstream impacts of the NAO in midwinter.

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Wenhui Xu, Chenghu Sun, Jingqing Zuo, Zhuguo Ma, Weijing Li, and Song Yang

Abstract

Maps of observed ground surface temperature (GST) in China generally contain inhomogeneities due to relocation of the observation site, changes in observation method, transition to automatic instruments, and so on. By using the observations of collocated manual and automatic weather stations in China, bias in daily GST caused by the transition to automatic observation systems is corrected for the first time in the present work. Then, the inhomogeneities caused by nonclimatic factors (e.g., relocation of the station and change of observation time) in the historical records of monthly GST are further reduced by using the penalized maximal F-test method. Analysis based on this new homogenized dataset reveals that the trend of annual-mean GST in China is approximately 0.273°C decade−1 during 1961–2016. The warming trend is stronger in winter (0.321°C decade−1) and spring (0.312°C decade−1) and weakest in summer (0.173°C decade−1). Spatially, all the stations in China, except for a few stations in southern China, present warming trends in the annual mean and in spring, fall, and winter seasons. In summer, cooling trends are observed in central and southern China. Moreover, we assess the monthly GST from five reanalysis products of the Global Land Data Assimilation System (GLDAS) during 1980–2016. The warming trends of Noah and the Catchment Land Surface Model (CLSM) from GLDAS-V2.0 are the closest to those of the homogenized observation, while the linear trends in the other three products (Noah, CLM, and MOS) from GLDAS-V1 are obviously different from those of the homogenized observation. Also, it is found that the spatial distribution of the warming trend is substantially overestimated in central China but underestimated in the other regions of China in these five GLDAS reanalysis products.

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Rongqing Han, Hui Wang, Zeng-Zhen Hu, Arun Kumar, Weijing Li, Lindsey N. Long, Jae-Kyung E. Schemm, Peitao Peng, Wanqiu Wang, Dong Si, Xiaolong Jia, Ming Zhao, Gabriel A. Vecchi, Timothy E. LaRow, Young-Kwon Lim, Siegfried D. Schubert, Suzana J. Camargo, Naomi Henderson, Jeffrey A. Jonas, and Kevin J. E. Walsh

Abstract

An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation.

Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño—namely, eastern Pacific (EP) and central Pacific (CP) El Niño—and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño.

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