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Xu Qin, Jiang-she Zhang, and Xiao-dong Yan

Abstract

In this paper, the authors propose two improved mixture Weibull distribution models by adding one or two location parameters to the existing two-component mixture two-parameter Weibull distribution [MWbl(2, 2)] model. One improved model is the mixture two-parameter Weibull and three-parameter Weibull distribution [MWbl(2, 3)] model. The other improved model is the two-component mixture three-parameter Weibull distribution [MWbl(3, 3)] model. In contrast to existing literature, which has focused on the MWbl(2, 2) and the typical Weibull distribution models, the authors apply the MWbl(2, 3) model and MWbl(3, 3) model to fit the distribution of wind speed data with nearly zero percentages of null wind speed. The parameters of the two improved models are estimated by the maximum likelihood method in which the maximization problem is regarded as a nonlinear programming problem with only inequality constraints and is solved numerically by the interior-point method. The experimental results show that the mixture Weibull models proposed in this paper are more flexible than the existing models for the analysis of wind speed data in practice.

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Muattar Saydi, Guoping Tang, Yan Qin, Hong Fang, and Xiaohua Chen

Abstract

Snow fraction has a direct impact on water resources in arid regions. The selection of proper methods for estimating snow fraction is thus essential. Two temperature-based and two humidity-based approaches to discriminate precipitation phase were evaluated using daily meteorological observations over the past six decades in Xinjiang in arid northwest China. The main findings included that 1) the finest discrimination was achieved by the wet-bulb temperature (T w) method whereas the single temperature threshold at 0°C produces the poorest result; the performances of the Dai and humidity-dependent empirical method (T RH) methods were between them, with slightly lower error using the Dai method. Also, the T w method is the least sensitive to regional heterogeneity and less affected by distinct changes in elevation; the other three methods, however, are biased mostly toward underestimating snow and show larger variations due to the regional discrepancies. Careful adjustment of snow discrimination thresholds based on the local properties of observation spots is needed for these methods. 2) Despite widespread warming, snow fraction perturbations in Xinjiang are characterized mainly by insignificant changes plus pronounced reductions at high mountain sites. Proxy drivers of such changes can be better explained by considering the hydrothermal diversity and changing climatic factors. Across the wetter subregions, snowfall has been significantly increasing, and the positive impact of which on snow fraction was hindered by significant warming, particularly in winter, and summer rainfall; across the drier subregions, however, insignificant change in snow fraction corresponds to a slow and insignificant increase in snowfall joined by the negative impacts of significant winter warming and summer rainfall.

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Panfeng Zhang, Guoyu Ren, Yan Xu, Xiaolan L. Wang, Yun Qin, Xiubao Sun, and Yuyu Ren

Abstract

This paper presents an analysis of changes in global land extreme temperature indices (1951–2015) based on the new global land surface daily air temperature dataset recently developed by the China Meteorological Administration (CMA). The linear trends of the gridpoint time series and global land mean time series were calculated by using a Mann–Kendall method that accounts for the lag-1 autocorrelation in the time series of annual extreme temperature indices. The results, which are generally consistent with previous studies, showed that the global land average annual and seasonal mean extreme temperature indices series all experienced significant long-term changes associated with warming, with cold threshold indices (frost days, icing days, cold nights, and cold days) decreasing, warm threshold indices (summer days, tropical nights, and warm days) increasing, and all absolute indices (TXx, TXn, TNx, and TNn) also increasing, over the last 65 years. The extreme temperature indices series based on daily minimum temperatures generally had a stronger and more significant trend than those based on daily maximum temperatures. The strongest warming occurred after the mid-1970s, and a few extreme temperature indices showed no significant trend over the period from 1951 to the mid-1970s. Most parts of the global land experienced significant warming trends over the period 1951–2015 as a whole, and the largest trends appeared in mid- to high latitudes of the Eurasian continent.

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Qin Zhang, Arun Kumar, Yan Xue, Wanqiu Wang, and Fei-Fei Jin

Abstract

Simulations from the National Centers for Environmental Prediction (NCEP) coupled model are analyzed to document and understand the behavior of the evolution of the El Niño–Southern Oscillation (ENSO) cycle. The analysis is of importance for two reasons: 1) the coupled model used in this study is also used operationally to provide model-based forecast guidance on a seasonal time scale, and therefore, an understanding of the ENSO mechanism in this particular coupled system could also lead to an understanding of possible biases in SST predictions; and 2) multiple theories for ENSO evolution have been proposed, and coupled model simulations are a useful test bed for understanding the relative importance of different ENSO mechanisms.

The analyses of coupled model simulations show that during the ENSO evolution the net surface heat flux acts as a damping mechanism for the mixed-layer temperature anomalies, and positive contribution from the advection terms to the ENSO evolution is dominated by the linear advective processes. The subsurface temperature–SST feedback, referred to as thermocline feedback in some theoretical literature, is found to be the primary positive feedback, whereas the advective feedback by anomalous zonal currents and the thermocline feedback are the primary sources responsible for the ENSO phase transition in the model simulation. The basic mechanisms for the model-simulated ENSO cycle are thus, to a large extent, consistent with those highlighted in the recharge oscillator. The atmospheric anticyclone (cyclone) over the western equatorial northern Pacific accompanied by a warm (cold) phase of the ENSO, as well as the oceanic Rossby waves outside of 15°S–15°N and the equatorial higher-order baroclinic modes, all appear to play minor roles in the model ENSO cycles.

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Yan Wang, Kun Yang, Zhengyang Pan, Jun Qin, Deliang Chen, Changgui Lin, Yingying Chen, Lazhu, Wenjun Tang, Menglei Han, Ning Lu, and Hui Wu

Abstract

The southern Tibetan Plateau (STP) is the region in which water vapor passes from South Asia into the Tibetan Plateau (TP). The accuracy of precipitable water vapor (PWV) modeling for this region depends strongly on the quality of the available estimates of water vapor advection and the parameterization of land evaporation models. While climate simulation is frequently improved by assimilating relevant satellite and reanalysis products, this requires an understanding of the accuracy of these products. In this study, PWV data from MODIS infrared and near-infrared measurements, AIRS Level-2 and Level-3, MERRA, ERA-Interim, JRA-55, and NCEP final reanalysis (NCEP-Final) are evaluated against ground-based GPS measurements at nine stations over the STP, which covers the summer monsoon season from 2007 to 2013. The MODIS infrared product is shown to underestimate water vapor levels by more than 20% (1.84 mm), while the MODIS near-infrared product overestimates them by over 40% (3.52 mm). The AIRS PWV product appears to be most useful for constructing high-resolution and high-quality PWV datasets over the TP; particularly the AIRS Level-2 product has a relatively low bias (0.48 mm) and RMSE (1.83 mm) and correlates strongly with the GPS measurements (R = 0.90). The four reanalysis datasets exhibit similar performance in terms of their correlation coefficients (R = 0.87–0.90), bias (0.72–1.49 mm), and RMSE (2.19–2.35 mm). The key finding is that all the reanalyses have positive biases along the PWV seasonal cycle, which is linked to the well-known wet bias over the TP of current climate models.

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