Yuchun Chen

Wind-Driven South China Sea Deep Basin Warm-Core/Cool-Core Eddies

The formation of the South China Sea (SCS) deep basin warm-core and cool-core eddies was studied numerically using the Princeton Ocean Model (POM) with 20 km horizontal resolution and 23 sigma levels conforming to a realistic bottom topography. Numerical integration was divided into pre-experimental and experimental stages. During the pre-experimental stage, we integrated the POM model for three years from zero velocity and April temperature and salinity climatological fields with climatological monthly mean wind stresses, restoring type surface salt and heat fluxes, and observational oceanic inflow/outflow at the open boundaries. During the experimental stage, we integrated the POM model for another 16 months under three different conditions: one control and two sensitivity runs (no-wind and no lateral transport). We take the fields of the last 12 months for analysis. The simulation under control run agrees well with earlier observational studies on the South China Sea surface thermal variabilities. In addition, the sensitivity study further confirms that the wind effect is the key factor for generation of the SCS deep basin warm/cool eddy and that the lateral boundary forcing is the major factor for the formation of the strong western boundary currents, especially along the southeast Chinese coast during both summer and winter monsoon seasons.

Particulate air pollution in Lanzhou China

Concentrations of total suspended particles (TSP) and PM(10) in Lanzhou China have been kept high for the past two decades. Data collected during the intensive observational period from October 1999 to April 2001 show high TSP and PM(10) concentrations. Starting from November, the PM(10) pollution intensifies, and reaches mid to high alert level of air pollution, continues until April next year, and is at low alert level in the summer. In the winter and spring, the TSP concentration is 2-10 times higher than the third-level criterion of air quality (severe pollution). Effects of intrinsic factors (sources of pollution) and remote preconditions (propagation of dust storms) for severe PM(10) and TSP pollution in Lanzhou are analyzed.

South China Sea Wind-Wave Characteristics. Part I: Validation of Wavewatch-III Using TOPEX/Poseidon Data

A full-spectral third-generation ocean wind-wave model, Wavewatch-III, has been implemented in the South China Sea (SCS) for investigating wind-wave characteristics. This model was developed at the Ocean Modeling Branch of the National Centers for Environmental Prediction (NCEP). The NASA QuickSCAT data (0.25degrees resolution) 2 times daily were used to simulate the wind waves for the entire year of 2000. The significant wave heights from Wavewatch-III are compared to the TOPEX/Poseidon (T/P) significant wave height data over the satellite crossover points in SCS. The model errors of significant wave height have Gaussian-type distribution with a small mean value of 0.02 m (almost no bias). The model errors are comparable to the T/P altimeter accuracy (0.5 m) in the central SCS and are smaller than the T/P altimeter accuracy in the northern and southern SCS, which indicates the capability of Wavewatch-III for SCS wave simulation.

Evaluation of the Princeton Ocean Model Using South China Sea Monsoon Experiment (SCSMEX) Data

The Princeton Ocean Model (POM) has been implemented in the South China Sea for hindcast of circulation and thermohaline structure. A two-step technique is used to initialize POM with temperature, salinity, and velocity for 1 April 1998 and integrate it from 1 April 1998 with synoptic surface forcing for 3 months with and without data assimilation. Hydrographic and current data acquired from the South China Sea Monsoon Experiment (SCSMEX) from April through June 1998 are used to verify, and to assimilate into, POM. The mean SCSMEX data (Apr‐Jun 1998) are about 0.58C warmer than the mean climatological data above the 50-m depth, and slightly cooler than the mean climatological data below the 50-m depth, and are fresher than the climatological data at all depths and with the maximum bias (0.2‐0.25 ppt) at 75-m depth. POM without data assimilation has the capability to predict the circulation pattern and the temperature field reasonably well, but has no capability to predict the salinity field. The model errors have Gaussian-type distribution for temperature hindcast, and non-Gaussian distribution for salinity hindcast with six to eight times more frequencies of occurrence on the negative side than on the positive side. Data assimilation enhances the model capability for ocean hindcast, if even only conductivity‐temperature‐depth (CTD) data are assimilated. When the model is reinitialized using the assimilated data at the end of a month (30 Apr; 31 May 1998) and the model is run for a month without data assimilation (hindcast capability test), the model errors for both temperature and salinity hindcast are greatly reduced, and they have Gaussian-type distributions for both temperature and salinity hindcast. Hence, POM gains capability in salinity hindcast when CTD data are assimilated.

Temporal and Spatial Variabilities of Japan Sea Surface Temperature and Atmospheric Forcings

In this study, we used the National Centers for Environmental Prediction monthly sea surface temperature (SST) and surface air temperature (SAT) data during 1982–1994 and the National Center for Atmospheric Research surface wind stress curl data during 1982–1989 to investigate the Japan Sea SST temporal and spatial variabilities and their relations to atmospheric forcing. First, we found an asymmetry in the correlation coefficients between SST and wind stress curl, which implies that the SST variability at the scales of the order of one month is largely due to atmospheric forcing. Second, we performed three analyses on the data fields: annual mean, composite analysis to obtain the monthly anomaly relative to the annual mean, and empirical orthogonal function (EOF) analysis on the residue data relative to the summation of the annual mean and the monthly anomaly. The first EOF mode of SST accounts for 59.9% of the variance and represents the Subpolar Front. The temporal variation of the first EOF mode implies that the deep Japan Sea could be cooler in cold seasons (November–April) of 1984–1987. Third, we computed cross-correlation coefficients among various principal components and found that the atmospheric warming/cooling is the key factor causing intra-seasonal and interannual SST variabilities.

On Haney-type surface thermal boundary conditions for ocean circulation models

Abstract Haney-type surface thermal boundary conditions linearly connect net downward surface heat flux Q to air–sea temperature difference (gradient-type condition) ΔT1 or to climate/synoptic sea temperature difference (restoring-type condition) ΔT2 by a coupling coefficient κ. In this study, the authors used the global reanalyzed data (6-h resolution) of Q, surface air temperature TA, and sea surface temperature TO from the National Centers for Environmental Prediction during 1 October 1994–31 December 1995 to verify the validity of Haney-type surface thermal boundary conditions. First, daily means of these variables were computed to get rid of diurnal variation. Second, the cross-correlation coefficients (CCC) between Q and (ΔT1, ΔT2) were calculated. The ensemble mean CCC fields show (i) no correlation between Q and ΔT2 anywhere in the world oceans, (ii) no correlation between Q and ΔT1 in the equatorial regions, and (c) evident correlation (CCC ≥ 0.7) between Q and ΔT1 in the middle and high latitude...

Japan Sea Thermohaline Structure and Circulation. Part III: Autocorrelation Functions

The autocorrelation functions of temperature and salinity in the three basins (Ulleung, Japan, and Yamato Basins) of the Japan/East Sea are computed using the U.S. Navy’s Master Oceanographic Observational Dataset for 1930‐97. After quality control the dataset consists of 93 810 temperature and 50 349 salinity profiles. The decorrelation scales of both temperature and salinity were obtained through fitting the autocorrelation function into the Gaussian function. The signal-to-noise ratios of temperature and salinity for the three basins are usually larger than 2. The signal-to-noise ratio of temperature is greater in summer than in winter. There is more noise in salinity than in temperature. This might be caused by fewer salinity than temperature observations. The autocorrelation functions of temperature for the three basins have evident seasonal variability at the surface: less spatial variability in the summer than in the winter. The temporal (spatial) decorrelation scale is shorter (longer) in the summer than in the winter. Such a strong seasonal variability at the surface may be caused by the seasonal variability of the net surface heat flux. The autocorrelation functions of salinity have weaker seasonal variability than those of the temperature field. The temporal and horizontal decorrelation scales obtained in this study are useful for designing an optimal observational network.

Numerical simulation of the critical scale of oasis maintenance and development in the arid regions of Northwest China

Oasis is a special geographic landscape among the vast desert/Gobi in Northwest China (NWC). The surface sensitive heat flux and latent heat flux at Zhangye Oasis during 1 to 11 August 1991 are simulated using the NCAR nonhydrostatic mesoscale model MM5 Version 3. The horizontal grid resolution is set as 1km. By comparing the simulation results with HEIFE observations, it is proved that the model can be used to simulate the surface energy and water mass exchange of arid and semiarid regions in NWC. Based on the above results, the influence of different oasis scales on the local atmospheric field near the ground surface, and the critical scale of oasis maintenance, in NWC are studied dynamically. The following conclusion is obtained: the local thermal circulation between the oasis and the desert/Gobi is formed in the oasis downstream if the oasis scale is larger than 4 km. This local thermal circulation between the oasis and the desert adjacent to the oasis helps to conserve water vapor over the oasis. At the same time, it transfers the abundant water vapor from the oasis into the desert/Gobi near to the oasis to supply relatively plentiful water vapor for desert crops to grow on the fringe of the oasis. So, it is advantageous for oasis extension. However, if the scale of the oasis is smaller than 4 km, it is not easy for the local thermal circulation between the oasis and the desert/Gobi to take shape. This study provides a new standpoint for oasis maintenance and development.

A Coastal Air-Ocean Coupled System (CAOCS) Evaluated Using an Airborne Expendable Bathythermograph (AXBT) Data Set

A coastal atmosphere-ocean coupled system (CAOCS) is developed with Princeton Ocean Model (POM) as the oceanic component, and with National Center for Atmospheric Research (NCAR) regional climate model (RegCM2) as the atmospheric component. The model domain (98.84°–121.16°E, 3.06°S–25.07°N) covers the whole SCS and surrounding land and islands. The surface fluxes of water, heat (excluding solar radiation), and momentum are applied synchronously with opposite signs in the atmosphere and ocean. Flux adjustments are not used. The CAOCS model was verified using an intensive airborne expendable bathythermograph (AXBT) survey between 14–25 May 1995 over the majority of the SCS down to about 300-m depth.

Afforestation for reduction of NOX concentration in Lanzhou China

Lanzhou is one of the major industrial cities in northwest China, the capital of Gansu Province, and located at a northwest-to-southeast oriented valley basin with elevation about 1500-1600-m. Due to topographic and meteorological characteristics, Lanzhou is one of the most polluted cities in China. Meteorological conditions (low winds, stable stratification especially inversion), pollutant sources and sinks affect the air quality. Lanzhou government carried out afforestation and pollutant-source reduction (closing several heavy industrial factories) to improve the air quality for the past two decades. In this study, effect of afforestation on reducing the NO(X) concentration is investigated numerically using RAMS-HYPACT model.