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Binhui Liu, Ming Xu, Mark Henderson, Ye Qi, and Yiqing Li

Abstract

In analyzing daily climate data from 305 weather stations in China for the period from 1955 to 2000, the authors found that surface air temperatures are increasing with an accelerating trend after 1990. They also found that the daily maximum (T max) and minimum (T min) air temperature increased at a rate of 1.27° and 3.23°C (100 yr)−1 between 1955 and 2000. Both temperature trends were faster than those reported for the Northern Hemisphere, where T max and T min increased by 0.87° and 1.84°C (100 yr)−1 between 1950 and 1993. The daily temperature range (DTR) decreased rapidly by −2.5°C (100 yr)−1 from 1960 to 1990; during that time, minimum temperature increased while maximum temperature decreased slightly. Since 1990, the decline in DTR has halted because T max and T min increased at a similar pace during the 1990s. Increased minimum and maximum temperatures were most pronounced in northeast China and were lowest in the southwest. Cloud cover and precipitation correlated poorly with the decreasing temperature range. It is argued that a decline in solar irradiance better explains the decreasing range of daily temperatures through its influence on maximum temperature. With declining solar irradiance even on clear days, and with decreases in cloud cover, it is posited that atmospheric aerosols may be contributing to the changing solar irradiance and trends of daily temperatures observed in China.

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Wenfeng Lai, Jianping Gan, Ye Liu, Zhiqiang Liu, Jiping Xie, and Jiang Zhu

Abstract

To improve the forecasting performance in dynamically active coastal waters forced by winds, tides, and river discharges in a coupled estuary-shelf model off Hong Kong, a multivariable data assimilation (DA) system using the ensemble optimal interpolation (EnOI) method has been developed and implemented. The system assimilates the Conductivity-Temperature-Depth (CTD) profilers, time-series buoy measurement, and remote sensing sea surface temperature (SST) data into a high-resolution estuary-shelf ocean model around Hong Kong. We found that the time window selection associated with the local dynamics and the number of observation samples are two key factors in improving assimilation in the unique estuary-shelf system. DA with a varied assimilation time window based on the intra-tidal variation in the local dynamics can reduce the errors in the estimation of the innovation vector caused by the model-observation mismatch at the analysis time, and improve greatly simulation in both the estuary and coastal regions. Statistically, the overall root-mean-square error (RMSE) between the DA forecasts and not-yet-assimilated observations for temperature and salinity have been reduced by 33.0% and 31.9% in the experiment period, respectively. By assimilating higher resolution remote sensing SST data instead of lower resolution satellite SST, the RMSE of SST is improved by ~18%. Besides, by assimilating real-time buoy mooring data, the model bias can be continuously corrected both around the buoy location and beyond. The assimilation of the combined buoy, CTD, and SST data can provide an overall improvement of the simulated three-dimensional solution. A dynamics-oriented assimilation scheme is essential for the improvement of model forecasting in the estuary-shelf system under multiple forcings.

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Li Liu, Ruizhe Li, Guangwen Yang, Bin Wang, Lijuan Li, and Ye Pu

Abstract

The rapid development of science and technology has enabled finer and finer resolutions in atmospheric general circulation models (AGCMs). Parallelization becomes progressively more critical as the resolution of AGCMs increases. This paper presents a new parallel version of the finite-difference Gridpoint Atmospheric Model of the Institute of Atmospheric Physics (IAP)–State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG; GAMIL) with various parallel optimization strategies, including two-dimensional hybrid parallel decomposition; hybrid parallel programming; parallel communications for coupling the physical packages, land surface, and dynamical core; and a cascading solution to the tridiagonal equations used in the dynamical core. The new parallel version under two different horizontal resolutions (1° and 0.25°) is evaluated. The new parallel version enables GAMIL to achieve higher parallel efficiency and utilize a greater number of CPU cores. GAMIL1° achieves 37.8% parallel efficiency using 960 CPU cores, while GAMIL0.25° achieves 57.5% parallel efficiency.

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Amin Salighehdar, Ziwen Ye, Mingzhe Liu, Ionut Florescu, and Alan F. Blumberg

Abstract

Accurate prediction of storm surge is a difficult problem. Most forecast systems produce multiple possible forecasts depending on the variability in weather conditions, possible temperature levels, winds, etc. Ensemble modeling techniques have been developed with the stated purpose of obtaining the best forecast (in some specific sense) from the individual forecasts. In this work a statistical methodology of evaluating the performance of multiple ensemble forecasting models is developed. The methodology is applied to predicting storm surge in the New York Harbor area. Data from three hurricane events collected from multiple locations in the New York Bay area are used. The methodology produces three key findings for the particular test data used. First, it is found that even the simplest possible way of creating an ensemble produces results superior to those of any single forecast. Second, for the data used and the events under study the methodology did not interact with any event at any location studied. Third, based on the methodology results for the data studied selecting the best-performing ensemble models for each specific location may be possible.

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Huilin Huang, Yongkang Xue, Nagaraju Chilukoti, Ye Liu, Gang Chen, and Ismaila Diallo

Abstract

Land-use and land-cover change (LULCC) is one of the most important forcings affecting climate in the past century. This study evaluates the global and regional LULCC impacts in 1950–2015 by employing an annually updated LULCC map in a coupled land–atmosphere–ocean model. The difference between LULCC and control experiments shows an overall land surface temperature (LST) increase by 0.48 K in the LULCC regions and a widespread LST decrease by 0.18 K outside the LULCC regions. A decomposed temperature metric (DTM) is applied to quantify the relative contribution of surface processes to temperature changes. Furthermore, while precipitation in the LULCC areas is reduced in agreement with declined evaporation, LULCC causes a southward displacement of the intertropical convergence zone (ITCZ) with a narrowing by 0.5°, leading to a tripole anomalous precipitation pattern over the warm pool. The DTM shows that the temperature response in LULCC regions results from the competing effect between increased albedo (cooling) and reduced evaporation (warming). The reduced evaporation indicates less atmospheric latent heat release in convective processes and thus a drier and cooler troposphere, resulting in a reduction in surface cooling outside the LULCC regions. The southward shift of the ITCZ implies a northward cross-equatorial energy transport anomaly in response to reduced latent/sensible heat of the atmosphere in the Northern Hemisphere, where LULCC is more intensive. Tropospheric cooling results in the equatorward shift of the upper-tropospheric westerly jet in both hemispheres, which, in turn, leads to an equatorward narrowing of the Hadley circulation and ITCZ.

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Jiali Ju, Heng Dai, Chuanhao Wu, Bill X. Hu, Ming Ye, Xingyuan Chen, Dongwei Gui, Haifan Liu, and Jin Zhang

Abstract

Comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty at different spatiotemporal scales. Here, a hierarchical sensitivity analysis framework (HSAF) incorporated with a variance-based global sensitivity analysis is developed to quantify the spatiotemporal contributions of different uncertainties in hydrological impacts of climate change in two different climatic (humid and semiarid) basins in China. The uncertainty sources include 3 emission scenarios (ESs), 20 global climate models (GCs), 3 hydrological models (HMs), and the associated sensitive hydrological parameters (PAs) screened and sampled by the Morris and Latin hypercube sampling methods, respectively. The results indicate that the overall trend of uncertainty is PA > HM > GC > ES, but their uncertainties have discrepancies in projections of different hydrological variables. The HM uncertainty in annual and monthly discharge projections is generally larger than the PA uncertainty in the humid basin than semiarid basin. The PA has greater uncertainty in extreme hydrological event (annual peak discharge) projections than in annual discharge projections for both basins (particularly for the humid basin), but contributes larger uncertainty to annual and monthly discharge projections in the semiarid basin than humid basin. The GC contributes larger uncertainty in all the hydrological variables projections in the humid basin than semiarid basin, while the ES uncertainty is rather limited in both basins. Overall, our results suggest there is greater spatiotemporal variability of hydrological uncertainty in more arid regions.

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Yang Lang, Aizhong Ye, Wei Gong, Chiyuan Miao, Zhenhua Di, Jing Xu, Yu Liu, Lifeng Luo, and Qingyun Duan

Abstract

Seasonal predictions of precipitation and surface air temperature from the Climate Forecast System, version 2 (CFSv2), are evaluated against gridded daily observations from 1982 to 2007 over 17 hydroclimatic regions in China. The seasonal predictive skill is quantified with skill scores including correlation coefficient, RMSE, and mean bias for spatially averaged seasonal precipitation and temperature forecasts for each region. The evaluation focuses on identifying regions and seasons where significant skill exists, thus potentially contributing to skill in hydrological prediction. The authors find that the predictive skill of CFSv2 precipitation and temperature forecasts has a stronger dependence on seasons and regions than on lead times. Both temperature and precipitation forecasts show higher skill from late summer [July–September (JAS)] to late autumn [October–December (OND)] and from winter [December–February (DJF)] to spring [March–May (MAM)]. The skill of CFSv2 precipitation forecasts is low during summer [June–August (JJA)] and winter (DJF) over all of China because of low potential predictability of the East Asian summer monsoon and the East Asian winter monsoon for China. As expected, temperature predictive skill is much higher than precipitation predictive skill in all regions. As observed precipitation shows significant correlation with the Oceanic Niño index over western, southwestern, and central China, the authors found that CFSv2 precipitation forecasts generally show similar correlation pattern, suggesting that CFSv2 precipitation forecasts can capture ENSO signals. This evaluation suggests that using CFSv2 forecasts for seasonal hydrological prediction over China is promising and challenging.

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Yi Liu, Ye Zhu, Liliang Ren, Jason Otkin, Eric D. Hunt, Xiaoli Yang, Fei Yuan, and Shanhu Jiang

Abstract

Flash droughts are extreme phenomena that have been identified using two different approaches. The first approach identifies these events based on unusually rapid intensification rates, whereas the second approach implicitly identifies short-term features. This latter approach classifies flash droughts into two types, namely, precipitation deficit and heat wave flash droughts (denoted as PDFD and HWFD). In this study, we evaluate these two approaches over the Yellow River basin (YRB) to determine which approach provides more accurate information about flash droughts and why. Based on the concept of intensification rate, a new quantitative flash drought identification method focused on soil moisture depletion during the onset–development phase is proposed. Its performance was evaluated by comparing the onset time and spatial dynamics of the identified flash droughts with PDFD and HWFD events identified using the second approach. The results show that the rapid-intensification approach is better able to capture the continuous evolution of a flash drought. Since the approach for identifying PDFD and HWFD events does not consider changes in soil moisture with time, it cannot ensure that the events exhibit rapid intensification, nor can it effectively capture flash droughts’ onset. Evaluation of the results showed that the chosen hydrometeorological variables and corresponding thresholds, particularly that of temperature, are the main reasons for the poor performance of the PDFD and HWFD identification approach. This study promotes a deeper understanding of flash droughts that is beneficial for drought monitoring, early warning, and mitigation.

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