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Hong Yin and Ying Sun

Abstract

Threshold indices of extreme temperature are defined based on temperature values that fall above or below fixed thresholds and thus have important implications for agriculture, engineering, and human health. Here, we focus on four extreme temperature fixed threshold indices and their detection and attribution at the global and continental scales, as well as within China. These indices include the number of days with daily minimum temperatures below 0°C [frost days (FD)] and above 20°C [tropical nights (TR)] and the number of days with daily maximum temperatures below 0°C [ice days (ID)] and above 25°C [summer days (SU)]. We employ an optimal fingerprinting method to compare the spatial and temporal changes in these fixed threshold indices assessed from observations and simulations performed with multiple models. We find that an anthropogenic signal can be robustly detected in these fixed threshold indices at scales of over the globe, most of the continents, and China. A natural signal cannot be identified in the changes in most of the indices, thus indicating the dominant role of anthropogenic forcing in producing these changes. In North and South America, the models show poor performance in reproducing the fixed threshold indices related to daily maximum temperature. The changes in summer days are not clearly related to their responses to external forcing over these two continents. This study provides a useful complement to other detection studies and sheds light on the importance of anthropogenic forcing in determining most of the fixed threshold indices at the global scale and over most of the continents, compared with internal variability.

Open access
Chi-Cherng Hong, Tim Li, LinHo, and Yin-Chen Chen

Abstract

A basinwide warming (cooling) in the Indian Ocean is observed following the El Niño (La Niña) mature phase, with the amplitude of the warming being significantly larger than the cooling. A composite analysis reveals that the amplitude asymmetry (positive skewness) between the warm and cold Indian Ocean basinwide sea surface temperature anomaly pattern (IOB) appears only when ENSO is concurrent with the Indian Ocean dipole (IOD). The amplitude asymmetry becomes insignificant during the ENSO-only and the IOD-only events.

The physical mechanism for the amplitude asymmetry is investigated by analyzing the mixed layer heat budget based on the Simple Ocean Data Assimilation (SODA) 2.0.2 data. It is found that the positive skewness in the IOD west pole (IODW) is mainly caused by the asymmetry of ocean temperature advection, whereas the positive skewness in the IOD east pole (IODE) is caused by the asymmetry of the surface heat flux anomaly (primarily shortwave radiation) in response to the ENSO remote forcing.

The asymmetry of the mixed layer depth (MLD) between warm and cold events is another factor contributing to the IOB positive skewness. The MLD in IODE during the warm events (27 m) is shallower than that of the cold events (45 m), resulting a larger (smaller) temperature tendency during the warm (cold) events. In contrast, the MLD in IODW during the warm events (44 m) is deeper than that of the cold events (37 m). Because the positive skewness in IODW is caused by the ocean temperature advection and the surface heat flux plays a damping role, a larger (smaller) MLD leads to a weaker (stronger) thermodynamic damping. Thus the asymmetry of MLD in both IODE and IODW favors a greater basinwide warming than cooling.

Full access
Ying Sun, Lianchun Song, Hong Yin, Botao Zhou, Ting Hu, Xuebin Zhang, and Peter Stott
Full access
Yu Wang, Hong-Qing Wang, Lei Han, Yin-Jing Lin, and Yan Zhang

Abstract

This study was designed to provide basic information for the improvement of storm nowcasting. According to the mean direction deviation of storm movement, storms were classified into three types: 1) steady storms (S storms, extrapolated efficiently), 2) unsteady storms (U storms, extrapolated poorly), and 3) transitional storms (T storms). The U storms do not fit the linear extrapolation processes because of their unsteady movements. A 6-yr warm-season radar observation dataset was used to highlight and analyze the differences between U storms and S storms. The analysis included geometric features, dynamic factors, and environmental parameters. The results showed that storms with the following characteristics changed movement direction most easily in the Beijing–Tianjin region: 1) smaller storm area, 2) lower thickness (echo-top height minus base height), 3) lower movement speed, 4) weaker updrafts and the maximum value located in the mid- and upper troposphere, 5) storm-relative vertical wind profiles dominated by directional shear instead of speed shear, 6) lower relative humidity in the mid- and upper troposphere, and 7) higher surface evaporation and ground roughness.

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Qiong Wu, Hong-Qing Wang, Yi-Zhou Zhuang, Yin-Jing Lin, Yan Zhang, and Sai-Sai Ding

Abstract

Three infrared (IR) indicators were included in this study: the 10.8-μm brightness temperature (BT10.8), the BT difference between 12.0 and 10.8 μm (BTD12.0–10.8), and the BT difference between 6.7 and 10.8 μm (BTD6.7–10.8). Correlations among these IR indicators were investigated using MTSAT-1R images for summer 2007 over East Asia. Temporal, spatial, and numerical frequency distributions were used to represent the correlations. The results showed that large BTD12.0–10.8 values can be observed in the growth of cumulus congestus and associated with the boundary of different terrain where convection was more likely to generate and develop. The results also showed that numerical correlation between any two IR indicators could be expressed by two-dimensional histograms (HT2D). Because of differences in the tropopause heights and in the temperature and water vapor fields, the shapes of the HT2Ds varied with latitude and the type of underlying surface. After carefully analyzing the correlations among the IR indicators, a conceptual model of the convection life cycle was constructed according to these HT2Ds. A new cloud convection index (CCI) was defined with the combination of BTD12.0–10.8 and BTD6.7–10.8 on the basis of the conceptual model. The preliminary test results demonstrated that CCI could effectively identify convective clouds. CCI value and its time trend could reflect the growth or decline of convective clouds.

Full access
Qiong Wu, Hong-Qing Wang, Yin-Jing Lin, Yi-Zhou Zhuang, and Yan Zhang

Abstract

An optical flow algorithm based on polynomial expansion (OFAPE) was used to derive atmospheric motion vectors (AMVs) from geostationary satellite images. In OFAPE, there are two parameters that can affect the AMV results: the sizes of the expansion window and optimization window. They should be determined according to the temporal interval and spatial resolution of satellite images. A helpful experiment was conducted for selecting those sizes. The limitations of window sizes can cause loss of strong wind speed, and an image-pyramid scheme was used to overcome this problem. Determining the heights of AMVs for semitransparent cloud pixels (STCPs) is challenging work in AMV derivation. In this study, two-dimensional histograms (H2Ds) between infrared brightness temperatures (6.7- and 10.8-μm channels) formed from a long time series of cloud images were used to identify the STCPs and to estimate their actual temperatures/heights. The results obtained from H2Ds were contrasted with CloudSat radar reflectivity and CALIPSO cloud-feature mask data. Finally, in order to verify the algorithm adaptability, three-month AMVs (JJA 2013) were calculated and compared with the wind fields of ERA data and the NOAA/ESRL radiosonde observations in three aspects: speed, direction, and vector difference.

Full access
Shi-Xin Wang, Hong-Chao Zuo, Fen Sun, Li-Yang Wu, Yixing Yin, and Jing-Jia Luo

Abstract

Dynamics of the East Asian spring rainband are investigated with a reanalysis dataset and station observations. Here, it is revealed that the rainband is anchored by external forcings. The midtropospheric jet core stays quasi-stationary around Japan. It has two branches in its entry region, which originate from the south and north flanks of the Tibetan Plateau and then run northeastward and southeastward, respectively. The southern branch advects warm air from the Tibetan–Hengduan Plateau northeastward, forming a rainband over southern China through causing adiabatic ascent motion and triggering diabatic feedback. The rainband is much stronger in spring than in autumn due to the stronger diabatic heating over the Tibetan–Hengduan Plateau, a more southward-displaced midtropospheric jet, and the resulting stronger warm advection over southern China. The northern jet branch forms a zonally elongated cold advection belt, which reaches a maximum around northern China, and then weakens and extends eastward to east of Japan. The westerly jet also steers strong disturbance activities roughly collocated with the cold advection belt via baroclinic instability. The high disturbance activities belt causes large cumulative warm advection (CWA) through drastically increasing extremely warm advection days on its eastern and south flanks, where weak cold advection prevails. CWA is more essential for monthly/seasonally rainfall than conventionally used time-average temperature advection because it is shown that strengthened warm advection can increase rainfall through positive diabatic feedback, while cold advection cannot cause negative rainfall. Thus, the rainband is collocated with the large CWA belt instead of the warm advection south of it. This rainband is jointed to the rainband over southern China, forming the long southwest–northeast-oriented East Asian spring rainband. Increasing moisture slightly displaces the rainband southeastward.

Restricted access
Jianping Duan, Lun Li, Zhuguo Ma, Jan Esper, Ulf Büntgen, Elena Xoplaki, Dujuan Zhang, Lily Wang, Hong Yin, and Jürg Luterbacher

Abstract

Large volcanic eruptions may cause abrupt summer cooling over large parts of the globe. However, no comparable imprint has been found on the Tibetan Plateau (TP). Here, we introduce a 400-yr-long temperature-sensitive network of 17 tree-ring maximum latewood density sites from the TP that demonstrates that the effects of tropical eruptions on the TP are generally greater than those of extratropical eruptions. Moreover, we found that large tropical eruptions accompanied by subsequent El Niño events caused less summer cooling than those that occurred without El Niño association. Superposed epoch analysis (SEA) based on 27 events, including 14 tropical eruptions and 13 extratropical eruptions, shows that the summer cooling driven by extratropical eruptions is insignificant on the TP, while significant summer temperature decreases occur subsequent to tropical eruptions. Further analysis of the TP August–September temperature responses reveals a significant postvolcanic cooling only when no El Niño event occurred. However, there is no such cooling for all other situations, that is, tropical eruptions together with a subsequent El Niño event, as well as extratropical eruptions regardless of the occurrence of an El Niño event. The averaged August–September temperature deviation (T dev) following 10 large tropical eruptions without a subsequent El Niño event is up to −0.48° ± 0.19°C (with respect to the preceding 5-yr mean), whereas the temperature deviation following 4 large tropical eruptions with an El Niño association is approximately 0.23° ± 0.16°C. These results indicate a mitigation effect of El Niño events on the TP temperature response to large tropical eruptions. The possible mechanism is that El Niño events can weaken the Indian summer monsoon with a subsequent decrease in rainfall and cooling effect, which may lead to a relatively high temperature on the TP, one of the regions affected by the Indian summer monsoon.

Open access
Cheng Qian, Jun Wang, Siyan Dong, Hong Yin, Claire Burke, Andrew Ciavarella, Buwen Dong, Nicolas Freychet, Fraser C. Lott, and Simon F. B. Tett
Open access