Previous studies suggest that the zonally averaged Hadley circulation (ZAHC) has experienced a robust poleward expansion, and its trend in intensity displays inconsistency among different data sets. This study examines changes in regional HC intensity and poleward edge using six reanalyses, outgoing longwave radiation, and precipitation data sets. HCs in six regions, including Africa (AFHC), the Indian Ocean (IOHC), the western Pacific (WPHC), the eastern Pacific (EPHC), South America (SAHC), and the Atlantic (ATHC), are investigated. Intensity trends in the Northern Hemisphere (NH) WPHC and ATHC and the Southern Hemisphere (SH) EPHC and ATHC are in agreement with each other in the six reanalyses. Furthermore, regional HCs in these domains appear to be intensifying, although not all of the reanalyses show statistically significant trends. For the poleward edge, its trend in the NH AFHC, IOHC, EPHC, SAHC, and ATHC is significantly larger than zero, and the northern HC poleward edge exhibits uniform poleward migrations in these five regions. In the SH, only the trend in the SAHC poleward edge is significantly different from zero. Furthermore, the trend in the SH SAHC poleward edge is significantly larger than those in the SH AFHC, IOHC, and ATHC. The results indicate that the poleward migration of the southern ZAHC poleward edge during recent decades that has been identified by previous studies may be attributed mainly to the poleward migration of the southern SAHC poleward edge. Further analyses suggest that changes in regional HC poleward edges could have a significant impact on regional precipitation anomalies.

Cardiovascular risk prediction functions offer an important diagnostic tool for clinicians and patients themselves. They are usually constructed with the use of parametric or semi-parametric survival regression models. It is essential to be able to evaluate the performance of these models, preferably with summaries that offer natural and intuitive interpretations. The concept of discrimination, popular in the logistic regression context, has been extended to survival analysis. However, the extension is not unique. In this paper, we define discrimination in survival analysis as the model’s ability to separate those with longer event-free survival from those with shorter event-free survival within some time horizon of interest. This definition remains consistent with that used in logistic regression, in the sense that it assesses how well the model-based predictions match the observed data. Practical and conceptual examples and numerical simulations are employed to examine four C statistics proposed in the literature to evaluate the performance of survival models. We observe that they differ in the numerical values and aspects of discrimination that they capture. We conclude that the index proposed by Harrell is the most appropriate to capture discrimination described by the above definition. We suggest researchers report which C statistic they are using, provide a rationale for their selection, and be aware that comparing different indices across studies may not be meaningful.

Haze pollution is a serious air quality issue in China. Previous studies over the North China Plain (NCP) mainly focused on analysing the haze during boreal winter. However, the variation in haze during spring and the related factors remain unclear. This study investigates inter-annual variation of the spring haze, which is represented by the humidity-corrected dry extinction coefficient (DEC) over the NCP. During high DEC years, pronounced positive DEC anomalies appear over the NCP and its surrounding regions. Correspondingly, a notable anticyclonic anomaly is observed over Northeast Asia, inducing significant southeasterly wind anomalies over the NCP. The anomalous southeasterly winds reduce wind speed and increase relative humidity, which provides favourable meteorological conditions for accumulation and growth of aerosol pollutants. Further analysis shows that the North Atlantic Oscillation (NAO) contributes to the formation of anticyclonic anomalies over Northeast Asia via downstream propagating atmospheric wave trains. Additionally, positive sea surface temperature (SST) anomalies over the subtropical northeastern Atlantic Ocean may also impact the DEC variation. The linear barotropic model experiment further confirms the important role of subtropical northeastern Atlantic SST anomalies in contributing to the anomalous anticyclone over Northeast Asia and southerly wind anomalies over the NCP region, which are triggered via eastwards propagating atmospheric wave trains. This study gives support to the idea that the NAO and the North Atlantic SST may be potential predictors for the frequently observed springtime NCP haze variation.

Correcting the forecast bias of numerical weather prediction models is important for severe weather warnings. The refined grid forecast requires direct correction on gridded forecast products, as opposed to correcting forecast data only at individual weather stations. In this study, a deep learning method called CU-net is proposed to correct the gridded forecasts of four weather variables from the European Centre for Medium-Range Weather Forecast Integrated Forecasting System global model (ECMWF-IFS): 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction, with a forecast lead time of 24 h to 240 h in North China. First, the forecast correction problem is transformed into an image-to-image translation problem in deep learning under the CU-net architecture, which is based on convolutional neural networks. Second, the ECMWF-IFS forecasts and ECMWF reanalysis data (ERA5) from 2005 to 2018 are used as training, validation, and testing datasets. The predictors and labels (ground truth) of the model are created using the ECMWF-IFS and ERA5, respectively. Finally, the correction performance of CU-net is compared with a conventional method, anomaly numerical correction with observations (ANO). Results show that forecasts from CU-net have lower root mean square error, bias, mean absolute error, and higher correlation coefficient than those from ANO for all forecast lead times from 24 h to 240 h. CU-net improves upon the ECMWF-IFS forecast for all four weather variables in terms of the above evaluation metrics, whereas ANO improves upon ECMWF-IFS performance only for 2-m temperature and relative humidity. For the correction of the 10-m wind direction forecast, which is often difficult to achieve, CU-net also improves the correction performance.

Previous studies indicated that the meridional (northerly or southerly) wind anomalies over East China play an important role in modulating interannual variation of the winter haze pollution in the North China Plain (NCP) mainly via changing surface wind speed and humidity. Here, we report that the factors for the formation of the meridional wind anomalies over East China related to interannual variation of winter haze pollution experienced a significant interdecadal change around the mid‐1990s. Before the mid‐1990s, two upstream atmospheric wave trains contribute to generation of the meridional wind anomalies over East China via inducing significant geopotential height anomalies over northeast Asia. The first occurred over mid‐latitude Eurasia and propagated eastward into East Asia, resembling the East Atlantic‐west Russia (EAWR) pattern. The second propagated eastward along the subtropical Asian jet. Furthermore, during this period, the change in the intensity of East Asian trough (EAT) was closely linked with interannual variation of the winter haze variation in the NCP. By contrast, after the mid‐1990s, the atmospheric wave train along the Asian subtropical jet was not observed. Furthermore, the connection between the EAT intensity and the winter time NCP haze variation was weak. The mid‐latitude EAWR‐like pattern and the El Niño‐Southern Oscillation‐related sea surface temperature anomalies in the tropical Pacific were possible factors that explain the meridional wind anomalies over East China. Understanding the change in the atmospheric anomalies contributing to interannual variation of the haze is essential for the prediction of haze in the NCP.

[1] This study investigates the modulation of the Pacific Decadal Oscillation (PDO) on the predictability of interannual early summer south China rainfall (SCR) using high-quality station rainfall data. Of particular interest is the difference in impact between negative and positive phases of the PDO on the predictability of interannual early summer SCR. A clear difference in the correlation between the interannual early summer SCR and the preceding sea surface temperature (SST) over the Pacific Ocean appears in negative and positive phases of the PDO. In the negative PDO phase, the correlation between interannual early summer SCR and SST is dominated by a pattern with significant negative correlations in the subtropical western North Pacific and southeast Pacific and significant positive correlations in the tropical central Pacific. However, in the positive PDO phase, significant positive correlations are observed in the tropical eastern Pacific. It is found that, for each PDO phase, the preceding SST anomalies in some regions in the Pacific may act as predictors of the interannual early summer SCR. As such, a two-regime regression model for the relationship between interannual early summer SCR and preceding SST anomalies is established based on the negative and positive PDO phases using respective multiple linear regression models. Results suggest that the interannual early SCR is more predictable in PDO positive phase than in negative phase. It offers a support for the argument that a segmented statistical forecasting approach associated with the decadal modulation effect of the coupled ocean atmospheric mode should be adopted to forecast the early summer SCR.

This study investigates impacts of two types of La Niña events, eastern Pacific (EP) La Niña and central Pacific (CP) La Niña, on Australian summer rainfall during 1951–2009. Results show that Australian summer rainfall is sensitive to the change in the location of sea surface temperature (SST) anomalies in equatorial Pacific. During CP La Niña, maximum cold SST anomaly is located in the equatorial CP west of 150°W, and significant northeasterly wind anomalies tend to prevail over northeastern Australia during austral summer. This brings more moist and warm flow from the tropics to Australia and leads to significant positive rainfall anomalies over northern and eastern Australia. In contrast, during EP La Niña, maximum cold SST anomaly is confined to equatorial EP east of 150°W and atmospheric circulation anomalies tend to be weak. As a result, rainfall anomalies are generally weak over Australia in EP La Niña. The differences in the Australian summer rainfall anomalies between CP La Niña and EP La Niña are attributed to the differences in atmospheric circulation anomalies. Specifically, the atmospheric circulation anomalies over tropical Pacific tend to be stronger and located more westward in CP La Niña. Higher climatological SST in the equatorial CP than equatorial EP, larger magnitude and westward shift of cold SST anomaly centre in CP La Niña than EP Niña may explain stronger and westward shift of the atmospheric anomalies in CP La Niña. Atmospheric model numerical experiments confirm the contribution of stronger circulation response in CP La Niña to the positive rainfall anomalies in Australia. Results in this study suggest that it is important to classify the La Niña events into different types when predicting Australian summer rainfall.

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