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William Perrie and Liangming Wang

Abstract

The authors present a simple model for the dynamics that couple the atmospheric boundary layer and wind-generated waves. The model is empirically motivated by parameterizations for the sea state-dependent drag coefficient and sea surface roughness derived by Smith et al. from HEXOS measurements. Estimates are made for the effect the coupling dynamics has on predicted sea state parameters such as spectral wave energy and the air–sea flux of momentum. Results are verified with observations collected during the CAL/VAL experiment of Dobson and Vachon. The authors demonstrate that inclusion of the coupling dynamics systematically improves wave modeling. The effect of the coupling dynamics is particularly important for young waves in the presence of high wind speeds. A tendency to improve estimates of maximum wave heights is achieved.

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William Perrie and Liangming Wang

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Guohui Wang and William K. Dewar

Abstract

A quasigeostrophic point vortex numerical model is used to explore interactions of eddies and seamounts. The ultimate objective of this study is to assess the role of meddy–seamount interaction as an input to Mediterranean salt tongue maintenance. Secondary objectives are to clarify the dynamics of meddy–seamount interaction. The results suggest that meddies survive seamount collisions with 60%–70% of their initial cores remaining intact as coherent vortices. Given observed formation rates, it appears meddies, in their interactions with seamounts, inject between one-quarter and one-half of the salt anomaly necessary to sustain the Mediterranean salt tongue. Other considerations suggest the anomalous mass flux by meddies is comparable to that due to the mean flow. In summary, meddies are important to the maintenance of the salt tongue, although other mechanisms are needed. Coherent vortex transport, of which meddies are one example, is a mesoscale process not well described by the downgradient mixing algorithms normally employed in general circulation models. More sophisticated mesoscale models are thus suggested by this study. In particular, survival by meddies of collisions with seamounts emerges as a potentially important limiting effect on the Mediterranean salt tongue. This effect has climatically significant implications for ocean simulations.

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Junhong Wang and William B. Rossow

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A method is described to use rawinsonde data to estimate cloud vertical structure, including cloud-top and cloud-base heights, cloud-layer thickness, and the characteristics of multilayered clouds. Cloud-layer base and top locations are identified based on three criteria: maximum relative humidity in a cloud of at least 87%, minimum relative humidity of at least 84%, and relative humidity jumps exceeding 3% at cloud-layer top and base, where relative humidity is with respect to liquid water at temperatures greater than or equal to 0°C and with respect to ice at temperatures less than 0°C. The analysis method is tested at 30 ocean sites by comparing with cloud properties derived from other independent data sources. Comparison of layer-cloud frequencies of occurrence with surface observations shows that rawinsonde observations (RAOBS) usually detect the same number of cloud layers for low and middle clouds as the surface observers, but disagree more for high-level clouds. There is good agreement between the seasonal variations of RAOBS-determined top pressure of the highest cloud and that from the International Satellite Cloud Climate Project (ISCCP) data. RAOBS-determined top pressures of low and middle clouds agree better with ISCCP, but RAOBS often fail to detect very high and thin clouds. The frequency of multilayered clouds is qualitatively consistent with that estimated from surface observations. In cloudy soundings at these ocean sites, multilayered clouds occur 56% of the time and are predominately two layered. Multilayered clouds are most frequent (≈70%) in the Tropics (10°S–10°N) and least frequent at subtropical eastern Pacific stations. The frequency of multilayered clouds is higher in summer than in winter at low-latitude stations (30°S–30°N), but the opposite variation appears at the two subtropical stations. The frequency distributions of cloud top, cloud base, and cloud-layer thickness and cloud occurrence as a function of height are also presented. The lowest layer of multilayered cloud systems is usually located in the atmospheric boundary layer.

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Junhong Wang, William B. Rossow, and Yuanchong Zhang

Abstract

A global cloud vertical structure (CVS) climatic dataset is created by applying an analysis method to a 20-yr collection of twice-daily rawinsonde humidity profiles to estimate the height of cloud layers. The CVS dataset gives the vertical distribution of cloud layers for single and multilayered clouds, as well as the top and base heights and layer thicknesses of each layer, together with the original rawinsonde profiles of temperature, humidity, and winds. The average values are cloud-top height = 4.0 km above mean sea level (MSL), cloud-base height = 2.4 km MSL, cloud-layer thickness = 1.6 km, and separation distance between consecutive layers = 2.2 km. Multilayered clouds occur 42% of the time and are predominately two-layered. The lowest layer of multilayered cloud systems is usually located in the atmospheric boundary layer (below 2-km height MSL). Clouds over the ocean occur more frequently at lower levels and are more often formed in multiple layers than over land. Latitudinal variations of CVS also show maxima and minima that correspond to the locations of the intertropical convergence zone, the summer monsoons, the subtropical subsidence zones, and the midlatitude storm zones. Multilayered clouds exist most frequently in the Tropics and least frequently in the subtropics; there are more multilayered clouds in summer than in winter. Cloud layers are thicker in winter than in summer at mid- and high latitudes, but are thinner in winter in Southeast Asia.

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William B. Rossow, Yuanchong Zhang, and Junhong Wang

Abstract

To diagnose how cloud processes feed back on weather- and climate-scale variations of the atmosphere requires determining the changes that clouds produce in the atmospheric diabatic heating by radiation and precipitation at the same scales of variation. In particular, not only the magnitude of these changes must be quantified but also their correlation with atmospheric temperature variations; hence, the space–time resolution of the cloud perturbations must be sufficient to account for the majority of these variations. Although extensive new global cloud and radiative flux datasets have recently become available, the vertical profiles of clouds and consequent radiative flux divergence have not been systematically measured covering weather-scale variations from about 100 km, 3 h up to climate-scale variations of 10 000 km, decadal inclusive. By combining the statistics of cloud layer occurrence from the International Satellite Cloud Climatology Project (ISCCP) and an analysis of radiosonde humidity profiles, a statistical model has been developed that associates each cloud type, recognizable from satellite measurements, with a particular cloud vertical structure. Application of this model to the ISCCP cloud layer amounts produces estimates of low-level cloud amounts and average cloud-base pressures that are quantitatively closer to observations based on surface weather observations, capturing the variations with latitude and season and land and ocean (results are less good in the polar regions). The main advantage of this statistical model is that the correlations of cloud vertical structure with meteorology are qualitatively similar to “classical” information relating cloud properties to weather. These results can be evaluated and improved with the advent of satellites that can directly probe cloud vertical structures over the globe, providing statistics with changing meteorological conditions.

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Hailong Wang, William C. Skamarock, and Graham Feingold

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In the Advanced Research Weather Research and Forecasting Model (ARW), versions 3.0 and earlier, advection of scalars was performed using the Runge–Kutta time-integration scheme with an option of using a positive-definite (PD) flux limiter. Large-eddy simulations of aerosol–cloud interactions using the ARW model are performed to evaluate the advection schemes. The basic Runge–Kutta scheme alone produces spurious oscillations and negative values in scalar mixing ratios because of numerical dispersion errors. The PD flux limiter assures positive definiteness but retains the oscillations with an amplification of local maxima by up to 20% in the tests. These numerical dispersion errors contaminate active scalars directly through the advection process and indirectly through physical and dynamical feedbacks, leading to a misrepresentation of cloud physical and dynamical processes. A monotonic flux limiter is introduced to correct the generally accurate but dispersive solutions given by high-order Runge–Kutta scheme. The monotonic limiter effectively minimizes the dispersion errors with little significant enhancement of numerical diffusion errors. The improvement in scalar advection using the monotonic limiter is discussed in the context of how the different advection schemes impact the quantification of aerosol–cloud interactions. The PD limiter results in 20% (10%) fewer cloud droplets and 22% (5%) smaller cloud albedo than the monotonic limiter under clean (polluted) conditions. Underprediction of cloud droplet number concentration by the PD limiter tends to trigger the early formation of precipitation in the clean case, leading to a potentially large impact on cloud albedo change.

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Wen-Ssn Chuang, Dong-Ping Wang, and William C. Boicourt

Abstract

Low-frequency current variability on the continental shelf, 84 km off the mouth of the Chesapeake Bay, was examined from 4-month (mid-March to June 1975) current, sea level and meteorological records. Taking into account the seasonal change in wind stress and stratification, the record was divided into two 60-day periods. In both periods, the transient alongshore currents wore barotropic and coherent with sea level fluctuations.

During the first period (March and April 1975), winds were in the east–west direction, and the shelf water was homogeneous. At time scales longer than 4 days, sea level was a large-scale feature (coherent over the entire Mid-Atlantic Bight). At shorter time scales, sea level was driven by the local, alongshore wind. In contrast, the cross-shelf current, which was mainly barotropic, was driven by the alongshore wind at all time scales.

During the second period (May and June 1975), winds were in the north-south direction and the shelf water was stratified. Sea level was mainly driven by the local alongshore wind at all time scales. The cross-shelf current, which was baroclinic at time scales longer than 4 days, and barotropic at shorter time scales, was also driven by the alongshore wind.

The difference in response characteristics of the two periods indicate that circulation on the southern Mid-Atlantic Bight is strongly affected by local wind forcing, nonlocal effect, density stratification and the duration of alongshore wind.

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Kirk D. Poore, Junhong Wang, and William B. Rossow

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Cloud layer thicknesses are derived from base and top altitudes by combining 14 years (1975–1988) of surface and upper-air observations at 63 sites in the Northern Hemisphere. Rawinsonde observations are employed to determine the locations of cloud-layer top and base by testing for dewpoint temperature depressions below some threshold value. Surface observations serve as quality cheeks on the rawinsonde-determined cloud properties and provide cloud amount and cloud-type information. The dataset provides layer-cloud amount, cloud type, high, middle, or low height classes, cloud-top heights, base heights and layer thicknesses, covering a range of latitudes from 0° to 80°N. All data comes from land sites: 34 are located in continental interiors, 14 are near coasts, and 15 are on islands. The uncertainties in the derived cloud properties are discussed. For clouds classified by low-, mid-, and high-top altitudes, there are strong latitudinal and seasonal variations in the layer thickness only for high clouds. High-cloud layer thickness increases with latitude and exhibits different seasonal variations in different latitude zones: in summer, high-cloud layer thickness is a maximum in the Tropics but a minimum at high latitudes. For clouds classified into three types by base altitude or into six standard morphological types, latitudinal and seasonal variations in layer thickness are very small. The thickness of the clear surface layer decreases with latitude and reaches a summer minimum in the Tropics and summer maximum at higher latitudes over land, but does not vary much over the ocean. Tropical clouds occur in three base-altitude groups and the layer thickness of each group increases linearly with top altitude. Extratropical clouds exhibit two groups, one with layer thickness proportional to their cloud-top altitude and one with small (≤1000 m) layer thickness independent of cloud-top altitude.

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William B. Bennett, Jingfeng Wang, and Rafael L. Bras

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This study investigates the use of a previously published algorithm for estimating ground heat flux (GHF) at the global scale. The method is based on an analytical solution of the diffusion equation for heat transfer in a soil layer and has been shown to be effective at the point scale. The algorithm has several advantageous properties: 1) it only needs a single-level input of surface (skin) temperature, 2) the time-mean GHF can be derived directly from time-mean skin temperature, 3) it has reduced sensitivity to the variability in soil thermal properties and moisture, 4) it does not requires snow depth, and 5) it is computationally effective. A global map of the necessary thermal inertia parameter is derived using reanalysis data as a function of soil type. These parameter estimates are comparable to values obtained from in situ observations. The new global GHF estimates are generally consistent with the reanalysis GHF output simulated using two-layer soil hydrology models. The authors argue that the new algorithm is more robust and trustworthy in regions where they differ. The proposed algorithm offers potential benefits for direct assimilation of observations of surface temperature as well as GHF into the reanalysis models at various time scales.

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