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Yang Gao, Jian Lu, and L. Ruby Leung

Abstract

This study investigates the North Atlantic atmospheric rivers (ARs) making landfall over western Europe in the present and future climate from the multimodel ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Overall, CMIP5 captures the seasonal and spatial variations of historical landfalling AR days, with the large intermodel variability strongly correlated with the intermodel spread of historical near-surface westerly jet position. Under representative concentration pathway 8.5 (RCP8.5), AR frequency is projected to increase significantly by the end of this century, with 127%–275% increase at peak AR frequency regions (45°–55°N). While thermodynamics plays a dominant role in the future increase of ARs, wind changes associated with the midlatitude jet shifts also significantly contribute to AR changes, resulting in dipole change patterns in all seasons. In the North Atlantic, the model-projected jet shifts are strongly correlated with the simulated historical jet position. As models exhibit predominantly equatorward biases in the historical jet position, the large poleward jet shifts reduce AR days south of the historical mean jet position through the dynamical connections between the jet positions and AR days. Using the observed historical jet position as an emergent constraint, dynamical effects further increase future AR days over the equatorward flank above the increases from thermodynamical effects. Compared to the present, both total and extreme precipitation induced by ARs in the future contribute more to the seasonal mean and extreme precipitation, primarily because of the increase in AR frequency. While AR precipitation intensity generally increases more relative to the increase in integrated vapor transport, AR extreme precipitation intensity increases much less.

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Zhe Li, Dawen Yang, Bing Gao, Yang Jiao, Yang Hong, and Tao Xu

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The present study aims to evaluate three global satellite precipitation products [TMPA 3B42, version 7 (3B42 V7); TMPA 3B42 real time (3B42 RT); and Climate Prediction Center morphing technique (CMORPH)] during 2003–12 for multiscale hydrologic applications—including annual water budgeting, monthly and daily streamflow simulation, and extreme flood modeling—via a distributed hydrological model in the Yangtze River basin. The comparison shows that the 3B42 V7 data generally have a better performance in annual water budgeting and monthly streamflow simulation, but this superiority is not guaranteed for daily simulation, especially for flood monitoring. It is also found that, for annual water budgeting, the positive (negative) bias of the 3B42 RT (CMORPH) estimate is mainly propagated into the simulated runoff, and simulated evapotranspiration tends to be more sensitive to negative bias. Regarding streamflow simulation, both near-real-time products show a region-dependent bias: 3B42 RT tends to overestimate streamflow in the upper Yangtze River, and, in contrast, CMORPH shows serious underestimation in those downstream subbasins while it is able to effectively monitor streamflow into the Three Gorges Reservoir. Using 394 selected flood events, the results indicate that 3B42 RT and CMORPH have competitive performances for near-real-time flood monitoring in the upper Yangtze, but for those downstream subbasins, 3B42 RT seems to perform better than CMORPH. Furthermore, the inability of all satellite products to capture some key features of the July 2012 extreme floods reveals the deficiencies associated with them, which will limit their hydrologic utility in local flood monitoring.

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Yang Hong, Kuo-Lin Hsu, Soroosh Sorooshian, and Xiaogang Gao

Abstract

A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud features from infrared (10.7 μm) geostationary satellite imagery in estimating finescale (0.04° × 0.04° every 30 min) rainfall distribution. This algorithm processes satellite cloud images into pixel rain rates by 1) separating cloud images into distinctive cloud patches; 2) extracting cloud features, including coldness, geometry, and texture; 3) clustering cloud patches into well-organized subgroups; and 4) calibrating cloud-top temperature and rainfall (T bR) relationships for the classified cloud groups using gauge-corrected radar hourly rainfall data. Several cloud-patch categories with unique cloud-patch features and T bR curves were identified and explained. Radar and gauge rainfall measurements were both used to evaluate the PERSIANN CCS rainfall estimates at a range of temporal (hourly and daily) and spatial (0.04°, 0.12°, and 0.25°) scales. Hourly evaluation shows that the correlation coefficient (CC) is 0.45 (0.59) at a 0.04° (0.25°) grid scale. The averaged CC of daily rainfall is 0.57 (0.63) for the winter (summer) season.

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Yang Gao, Tongwen Wu, Jun Wang, and Shihao Tang

Abstract

The Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) mission core satellite provides the new-generation global observation of rain since 2014. The main objective of this paper is to evaluate the suitability and limitation of GPM-DPR level-2 products over China. The DPR rain rate products are compared with rain gauge data during the summers of 5 years (2014–18). The ground observation network is composed of more than 50 000 rain gauges. The DPR precipitation products for all scans (DPR_NS, DPR_MS, and DPR_HS) generally underestimate rain rates. However, DPR_MS agrees better with gauge estimates than DPR_NS and DPR_HS, yielding the lowest mean error, systematic deviation, and highest Pearson correlation coefficient. In addition, all three swath types show obvious overestimation over gauge estimates between 0.5 and 1 mm h−1 and underestimation when gauge estimates are larger than 1 mm h−1. The DPR_HS and DPR_MS agree better with gauge estimates below and above 2.5 mm h−1, respectively. A deeper investigation was carried out to analyze the variation of DPR_MS’s performance with respect to terrains over China. An obvious underestimation, relative to gauge estimates, occurs in Tibetan Plateau while a slight overestimation occurs in the North China Plain. Furthermore, our comprehensive analysis suggests that in Sichuan Basin, the DPR_MS exhibit the best agreement with gauge estimates.

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Junpeng Yuan, Yong Gao, Dian Feng, and Yali Yang

Abstract

From a basinwide perspective, the dominant mode of Indian Ocean tropical cyclone genesis (TCG) in September–November (SON) shows an equatorially symmetric east–west zonal dipole pattern, which can explain approximately 13% of the SON TCG variance. This zonal dipole TCG pattern is significantly related to the tripole pattern of the sea surface temperature anomalies (SSTAs) in the tropical Indo-Pacific Ocean (IPT). The IPT, which is a combined interbasin mode and presents a dipole pattern of SSTAs in the tropical Indian Ocean and El Niño–like SSTAs in the tropical Pacific Ocean, can influence the local Walker circulation and zonal dipole TCG pattern over the tropical Indian Ocean. Associated with a positive IPT phase, abnormal ascending (descending) motions are induced and favorable for more (less) water vapor transport to the lower–middle level in the western (eastern) tropical Indian Ocean; significant anticyclonic vorticity anomalies are evoked in the lower level over the eastern tropical Indian Ocean, and weak easterly vertical wind shear appears over the tropical Indian Ocean. Thus, abnormally strong upward motion, abundant water vapor in the lower–middle level, and weak vertical wind shear are favorable for more TCG in the western tropical Indian Ocean, while the combined negative contributions of the vertical motion, lower-level vorticity, and humidity terms result in less TCG in the eastern tropical Indian Ocean.

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Miaoni Gao, Bin Wang, Jing Yang, and Wenjie Dong

Abstract

The Yangtze–Huaihe River basin (YHRB) is the core region of sultry heat wave occurrence over China during peak summer [July and August (JA)]. The extremely hot and muggy weather is locally controlled by a descending high pressure anomaly connected to the western Pacific subtropical high. During 1961–2015, the heat wave days (HWDs) in JA over the YHRB exhibit large year-to-year and decadal variations. Prediction of the total number of HWDs in JA is of great societal and scientific importance. The summer HWDs are preceded by a zonal dipole SST tendency pattern in the tropical Pacific and a meridional tripole SST anomaly pattern over the North Atlantic. The former signifies a rapid transition from a decaying central Pacific El Niño in early spring to a developing eastern Pacific La Niña in summer, which enhances the western Pacific subtropical high and increases pressure over the YHRB by altering the Walker circulation. The North Atlantic tripole SST anomalies persist from the preceding winter to JA and excite a circumglobal teleconnection pattern placing a high pressure anomaly over the YHRB. To predict the JA HWDs, a 1-month lead prediction model is established with the above two predictors. The forward-rolling hindcast achieves a significant correlation skill of 0.66 for 1981–2015, and the independent forecast skill made for 1996–2015 reaches 0.73. These results indicate the source of predictability of summer HWDs and provide an estimate for the potential predictability, suggesting about 55% of the total variance may be potentially predictable. This study also reveals greater possibilities for dynamical models to improve their prediction skills.

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Xiaodong Chen, L. Ruby Leung, Yang Gao, and Ying Liu

Abstract

Sea surface temperature (SST) significantly modulates the precipitation and temperature over land, with important consequences on land surface processes such as snowpack. Compared to the impact of remote SST, the effect of nearshore/local SST is less well understood. In this study, the impact of local SST on the mountain snowpack of the U.S. West Coast is investigated using two 6-km regional climate simulations driven by the same lateral boundary conditions but with time-varying versus time-invariant and warmer local SSTs during 2003–15. Results show that local SST warming leads to warmer winter with more precipitation over the mountains. Meanwhile, the removal of SST temporal variability results in reduced temperature variability but increased precipitation variability. As a result, winter snow accumulation decreases by ~200 mm per season in the Cascade Mountains in the north but increases by ~100 mm per season in the Sierra Nevada in the south. Such a dipole response results from the competing effects of precipitation and temperature change at different elevations and are amplified by the enhanced atmospheric river moisture transport. To further delineate the relative contributions of different meteorological factors to the snowpack response, two neural network models were developed to predict the snow behaviors at seasonal and monthly scales. These models reveal the dominant influence of the total amount and the average temperature of precipitation on the snowpack response. These findings highlight the sensitivity of mountain snowpack to local SST in the western United States and underscore the importance of local SST and atmospheric rives to accurate snowpack estimations for water management.

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Daosheng Wang, Jicai Zhang, Ya Ping Wang, Xianqing Lv, Yang Yang, Daidu Fan, and Shu Gao

Abstract

The model parameters in the suspended cohesive sediment transport model are quite important for the accurate simulation of suspended sediment concentrations (SSCs). Based on a three-dimensional cohesive sediment transport model and its adjoint model, the in situ observed SSCs at four stations are assimilated to simulate the SSCs and to estimate the parameters in Hangzhou Bay in China. Numerical experimental results show that the adjoint method can efficiently improve the simulation results, which can benefit the prediction of SSCs. The time series of the modeled SSCs present a clear semidiurnal variation, in which the maximal SSCs occur during the flood tide and near the high water level due to the large current speeds. Sensitivity experiments prove that the estimated results of the settling velocity and resuspension rate, especially the temporal variations, are robust to the model settings. The temporal variations of the estimated settling velocity are negatively correlated with the tidal elevation. The main reason is that the mean size of the suspended sediments can be reduced during the flood tide, which consequently decreases the settling velocity according to Stokes’s law, and it is opposite in the ebb tide. The temporal variations of the estimated resuspension rate and the current speeds have a significantly positive correlation, which accords with the dynamics of the resuspension rate. The temporal variations of the settling velocity and resuspension rate are reasonable from the viewpoint of physics, indicating the adjoint method can be an effective tool for estimating the parameters in the sediment transport models.

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Yuhan Wang, Hanbo Yang, Dawen Yang, Yue Qin, Bing Gao, and Zhentao Cong

Abstract

Precipitation is a primary climate forcing factor in catchment hydrology, and its spatial distribution is essential for understanding the spatial variability of ecohydrological processes in a catchment. In mountainous areas, meteorological stations are generally too sparse to represent the spatial distribution of precipitation. This study develops a spatial interpolation method that combines meteorological observations and regional climate model (RCM) outputs. The method considers the precipitation–elevation relationship in the mountain region and the topographic effects, especially the mountain blocking effect. Furthermore, using this method, this study produced a 3-km-resolution precipitation dataset from 1960 to 2014 in the middle and upper reaches of the Heihe River basin located on the northern slope of the Qilian Mountains in the northeastern Tibetan Plateau. Cross validation based on the station observations showed that this method is reasonable. The rationality of the interpolated precipitation data was also evaluated by the catchment water balances using the observed river discharge and the actual evapotranspiration based on remote sensing. The interpolated precipitation data were compared with the China Gauge-Based Daily Precipitation Analysis product and the RCM output and was shown to be optimal. The results showed that the proposed method effectively used the information from the meteorological observations and the RCM simulations and provided the spatial distributions of daily precipitations with reasonable accuracy and high resolution, which is important for a distributed hydrological simulation at the catchment scale.

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Hui Gao, Song Yang, Arun Kumar, Zeng-Zhen Hu, Bohua Huang, Yueqing Li, and Bhaskar Jha

Abstract

The East Asian mei-yu (EAMY), which includes the mei-yu over eastern China, baiu over Japan, and changma over Korea, is an important component of the Asia summer monsoon system. The EAMY rain belt jumps northward to the Yangtze and Huaihe River valleys (in China), Japan, and Korea from mid-June to mid-July, with remarkable interannual variability. In this study, the variability and predictability of EAMY are investigated using the retrospective ensemble predictions of the NCEP Climate Forecast System (CFS). The CFS reasonably captures the centers, magnitude, northward jump, and other features of EAMY over most regions. It also reasonably simulates the interannual variations of EAMY and its main influencing factors such as the western Pacific subtropical high, the East Asian monsoon circulation, and El Niño–Southern Oscillation (ENSO). The CFS is skillful in predicting EAMY and related circulation patterns with a lead time of one month. An empirical orthogonal function analysis with maximized signal-to-noise ratio is applied to determine the most predictable patterns of EAMY. Furthermore, experiments in which the CFS is forced by observed sea surface temperature (SST) exhibit lower skill in EAMY simulation, suggesting the importance of ocean–atmosphere coupling in predicting EAMY.

The CFS, which exaggerates the precipitation over the southern–southeastern hills of the Tibetan Plateau, overestimates the relationship between EAMY and tropical–subtropical atmospheric circulation due to the overly strong ENSO signals in the model, whereas the experiments forced by observed SST produce a weaker relationship. On the contrary, the CFS underestimates the link of EAMY to higher-latitude processes. An increase in the horizontal resolution of the CFS is expected to reduce some of these errors.

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