Anomalous Western Pacific Subtropical High during Late Summer in Weak La Nia Years: Contrast between 1981 and 2013

Both 1981 and 2013 were weak La Niña years with a similar sea surface temperature (SST) anomaly in the tropical Pacific, yet the western Pacific subtropical high (WPSH) during August exhibited an opposite anomaly in the two years. A comparison indicates that, in the absence of a strong SST anomaly in the tropics, the cold advection from Eurasian high latitudes and the convection of the western Pacific warm pool play important roles in influencing the strength and position of the WPSH in August. In August 1981, the spatial pattern of 500 hPa geopotential height was characterized by a meridional circulation with a strong ridge in the Ural Mountains and a deep trough in Siberia, which provided favorable conditions for cold air invading into the lower latitudes. Accordingly, the geopotential height to the north of the WPSH was reduced by the cold advection anomaly from high latitudes, resulting in an eastward retreat of the WPSH. Moreover, an anomalous cyclonic circulation in the subtropical western Pacific, excited by enhanced warm pool convection, also contributed to the eastward retreat of the WPSH. By contrast, the influence from high latitudes was relatively weak in August 2013 due to a zonal circulation pattern over Eurasia, and the anomalous anticyclonic circulation induced by suppressed warm pool convection also facilitated the westward extension of the WPSH. Therefore, the combined effects of the high latitude and tropical circulations may contribute a persistent anomaly of the WPSH in late summer, despite the tropical SST anomaly being weak.     

Recent significant tornadoes in China

正1.Introduction Compared with the United States,whose annual number of tornadoes can exceed 1000,the average number of tornadoes per year in China over the past half a century is estimated to be fewer than 100(Fan and Yu,2015),even though both countries are located in a similar latitudinal zone of the Northern Hemisphere.The annual average recorded number of F1(Fujita scale)or EF1(enhanced Fujita scale)intensity or higher tornadoes in China between 1961 and 2010 is about     

A timescale decomposed threshold regression downscaling approach to forecasting South China early summer rainfall

A timescale decomposed threshold regression(TSDTR) downscaling approach to forecasting South China early summer rainfall(SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR.The two models are developed based on the partial least squares(PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915–84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation(PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985–2006, compared to other simpler approaches. This study suggests that the TSDTR approach,considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.     

Erratum to: Climate change projections over India by a downscaling approach using PRECIS

The author “Bhaski Bhaskaran” and his affiliation “Fujitsu Laboratory of Europe, Middlesex, UK” should be replaced by “Balakrishnan Bhaskaran”, “Fujitsu Laboratories of Europe Limited, Hayes Park, Middlesex, UK”, respectively.The corrected name and affiliation are shown in this erratum.     

Comparison of prediction performance using statistical postprocessing methods

As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.     

Long-term estimates of parameters of the vertical distribution of cloud layers from atmospheric radiosounding data

Based on the global aerological dataset and on the method for determination of the boundaries and amount of cloudiness using the profiles of temperature and humidity obtained from the atmospheric radiosounding data [23], the estimates are computed for the parameters of atmospheric temperature- humidity separation into cloud and intercloud layers from the surface to the height of 10 km. The base and top of cloud layers and their total thickness and frequency are selected as layering parameters. The computations are based on the data for the observational period of 1964-1998. To specify the spatiotemporal features of atmospheric layering, long-term geographic distributions of mean values and standard deviations of the mentioned parameters are constructed for January and July, and the amplitude of their variations is determined.     

Spatiotemporal variations in climate moisture indices in Georgia under global warming

Using observational data from 50 weather stations in Georgia for the period of 1936-2013, the following climate indices of moisture regime are studied: maximum 1-day precipitation, maximum 5-day precipitation, the simple daily intensity index, the number of days with precipitation equal to not less than 10, 20, and 50 mm, number of consecutive wet and dry days. Geoinformation maps of the spatial structure are plotted, and the dynamics of these indices is studied for the period of global warming. Expected changes in the moisture regime in different physiographic regions in Georgia are assessed.