Search Results

You are looking at 1 - 7 of 7 items for

  • Author or Editor: Zhiqiang Liu x
  • All content x
Clear All Modify Search
Zhiqiang Liu and Jianping Gan

Abstract

A three-dimensional, high-resolution numerical model is used to investigate processes and dynamics of an intensified upwelling that is induced by a coastal promontory over the East China Sea (ECS) shelf. The center of the intensified upwelling around the promontory has been constantly observed, but, so far, it has been dynamically unexplained. Forced by an idealized southeasterly wind stress, the model results well capture the observed upwelling at the lee of the coastal promontory. The intensified upwelling is formed by a strengthened shoreward transport downstream of the promontory as the upwelling jet veers shoreward. The jet is mainly controlled by a cross-shore geostrophic balance and is largely modulated by both centrifugal acceleration associated with nonlinear advection and by bottom stress. The strengthened shoreward transport is mainly attributed to the cross-shore geostrophic current that is induced by a countercurrent (negative) pressure gradient force (PGF) and partly attributed to the bottom Ekman transport. Based on the analyses of the momentum balance and depth-integrated vorticity dynamics, the authors provide a new explanation for the origin of negative PGF. It is found that the countercurrent PGF is generated by negative bottom stress curl and strengthened by negative vorticity advection downstream of the promontory. While the negative bottom stress curl arises from bottom shear vorticity, the source of negative advection downstream of the promontory is the negative shear vorticity on the seaside of the shoreward-bent jet. Nevertheless, cyclonic curvature vorticity at the bottom and positive vorticity advection in the water column at the promontory weakens the negative PGF. Although nonlinear advection strengthens vorticity advection, it weakens bottom stress curl and has little net effect on the countercurrent PGF.

Full access
Jianping Gan, Zhiqiang Liu, and Chiwing Rex Hui

Abstract

Understanding of the three-dimensional circulation in the South China Sea (SCS) is crucial for determining the transports of water masses, energy, and biogeochemical substances in the regional and adjacent larger oceans. The circulation’s kinematic and dynamic natures, however, are largely unclear. Results from a three-dimensional numerical ocean circulation model and geostrophic currents, derived from hydrographic data, reveal the existence of a unique, three-layer, cyclonic–anticyclonic–cyclonic (CAC) circulation in the upper (<750 m), middle (750–1500 m), and deep (>1500 m) layers in the SCS with differing seasonality. An inflow–outflow–inflow structure in Luzon Strait largely induces the CAC circulation, which leads to vortex stretching in the SCS basin because of a lateral planetary vorticity flux in each of the respective layers. The formation of joint effects of baroclinicity and relief (JEBAR) is an intrinsic dynamic response to the CAC circulation. The JEBAR arises from the CAC flow–topography interaction in the SCS.

Full access
Zhiqiang Chen, Jiping Liu, Mirong Song, Qinghua Yang, and Shiming Xu

Abstract

Here sea ice concentration derived from the Special Sensor Microwave Imager/Sounder and thickness derived from the Soil Moisture and Ocean Salinity and CryoSat-2 satellites are assimilated in the National Centers for Environmental Prediction Climate Forecast System using a localized error subspace transform ensemble Kalman filter (LESTKF). Three ensemble-based hindcasts are conducted to examine impacts of the assimilation on Arctic sea ice prediction, including CTL (without any assimilation), LESTKF-1 (with initial sea ice assimilation only), and LESTKF-E5 (with every 5-day sea ice assimilation). Assessment with the assimilated satellite products and independent sea ice thickness datasets shows that assimilating sea ice concentration and thickness leads to improved Arctic sea ice prediction. LESTKF-1 improves sea ice forecast initially. The initial improvement gradually diminishes after ~3-week integration for sea ice extent but remains quite steady through the integration for sea ice thickness. Large biases in both the ice extent and thickness in CTL are remarkably reduced through the hindcast in LESTKF-E5. Additional numerical experiments suggest that the hindcast with sea ice thickness assimilation dramatically reduces systematic bias in the predicted ice thickness compared with sea ice concentration assimilation only or without any assimilation, which also benefits the prediction of sea ice extent and concentration due to their covariability. Hence, the corrected state of sea ice thickness would aid in the forecast procedure. Increasing the number of ensemble members or extending the integration period to generate estimates of initial model states and uncertainties seems to have small impacts on sea ice prediction relative to LESTKF-E5.

Full access
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 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 that is based on the intratidal 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 simulation greatly 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 has 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.

Restricted access
Jiping Liu, Zhiqiang Chen, Jennifer Francis, Mirong Song, Thomas Mote, and Yongyun Hu

Abstract

In recent decades, the Greenland ice sheet has experienced increased surface melt. However, the underlying cause of this increased surface melting and how it relates to cryospheric changes across the Arctic remain unclear. Here it is shown that an important contributing factor is the decreasing Arctic sea ice. Reduced summer sea ice favors stronger and more frequent occurrences of blocking-high pressure events over Greenland. Blocking highs enhance the transport of warm, moist air over Greenland, which increases downwelling infrared radiation, contributes to increased extreme heat events, and accounts for the majority of the observed warming trends. These findings are supported by analyses of observations and reanalysis data, as well as by independent atmospheric model simulations using a state-of-the-art atmospheric model that is forced by varying only the sea ice conditions. Reduced sea ice conditions in the model favor more extensive Greenland surface melting. The authors find a positive feedback between the variability in the extent of summer Arctic sea ice and melt area of the summer Greenland ice sheet, which affects the Greenland ice sheet mass balance. This linkage may improve the projections of changes in the global sea level and thermohaline circulation.

Full access
Shunlin Liang, Jie Cheng, Kun Jia, Bo Jiang, Qiang Liu, Zhiqiang Xiao, Yunjun Yao, Wenping Yuan, Xiaotong Zhang, Xiang Zhao, and Ji Zhou

Abstract:

The Global Land Surface Satellite (GLASS) product suite currently contains 12 products, including leaf area index, fraction of absorbed photosynthetically active radiation, fraction of green vegetation coverage, gross primary production, broadband albedo, broadband longwave emissivity, downward shortwave radiation and photosynthetically active radiation, land surface temperature, downward and upwelling thermal radiation, all-wave net radiation, and evapotranspiration. These products are generated from the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer satellite data. Their unique features include long-term temporal coverage (many from 1981 to the present), high spatial resolutions of the surface radiation products (1 km and 0.05°), spatial continuities without missing pixels, and high quality and accuracy based on extensive validation using in situ measurements and intercomparisons with other existing satellite products. Moreover, the GLASS products are based on robust algorithms that have been published in peer-reviewed literature. Herein, we provide an overview of the algorithm development, product characteristics, and some preliminary applications of these products. We also describe the next steps, such as improving the existing GLASS products, generating more climate data records (CDRs), broadening product dissemination, and fostering their wider utilization. The GLASS products are freely available to the public.

Full access
David C. Leon, Jeffrey R. French, Sonia Lasher-Trapp, Alan M. Blyth, Steven J. Abel, Susan Ballard, Andrew Barrett, Lindsay J. Bennett, Keith Bower, Barbara Brooks, Phil Brown, Cristina Charlton-Perez, Thomas Choularton, Peter Clark, Chris Collier, Jonathan Crosier, Zhiqiang Cui, Seonaid Dey, David Dufton, Chloe Eagle, Michael J. Flynn, Martin Gallagher, Carol Halliwell, Kirsty Hanley, Lee Hawkness-Smith, Yahui Huang, Graeme Kelly, Malcolm Kitchen, Alexei Korolev, Humphrey Lean, Zixia Liu, John Marsham, Daniel Moser, John Nicol, Emily G. Norton, David Plummer, Jeremy Price, Hugo Ricketts, Nigel Roberts, Phil D. Rosenberg, David Simonin, Jonathan W. Taylor, Robert Warren, Paul I. Williams, and Gillian Young

Abstract

The Convective Precipitation Experiment (COPE) was a joint U.K.–U.S. field campaign held during the summer of 2013 in the southwest peninsula of England, designed to study convective clouds that produce heavy rain leading to flash floods. The clouds form along convergence lines that develop regularly as a result of the topography. Major flash floods have occurred in the past, most famously at Boscastle in 2004. It has been suggested that much of the rain was produced by warm rain processes, similar to some flash floods that have occurred in the United States. The overarching goal of COPE is to improve quantitative convective precipitation forecasting by understanding the interactions of the cloud microphysics and dynamics and thereby to improve numerical weather prediction (NWP) model skill for forecasts of flash floods. Two research aircraft, the University of Wyoming King Air and the U.K. BAe 146, obtained detailed in situ and remote sensing measurements in, around, and below storms on several days. A new fast-scanning X-band dual-polarization Doppler radar made 360° volume scans over 10 elevation angles approximately every 5 min and was augmented by two Met Office C-band radars and the Chilbolton S-band radar. Detailed aerosol measurements were made on the aircraft and on the ground. This paper i) provides an overview of the COPE field campaign and the resulting dataset, ii) presents examples of heavy convective rainfall in clouds containing ice and also in relatively shallow clouds through the warm rain process alone, and iii) explains how COPE data will be used to improve high-resolution NWP models for operational use.

Full access